⌘J History

OpenClaw Implementation Prompts

Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter.

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1) Personal CRM Intelligence

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Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach.

Data sources:\

• Connect to my email (Gmail API or IMAP) and scan the last 60 days of messages.\
• Connect to my calendar (Google Calendar API) and scan the last 60 days of events.\
• Run this ingestion on a daily cron schedule.
  
  Contact extraction from email:\
• Extract sender/recipient email addresses and names from messages.\
• Estimate the number of exchanges (back-and-forth threads, not just raw message count): Math.min(Math.floor(totalMessages / 2), threadCount).\
• Collect sample subject lines and message snippets for classification.
  
  Contact extraction from calendar:\
• Only include meetings with 1-10 attendees (skip large all-hands).\
• Only include meetings at least 15 minutes long (skip quick check-ins that are really just reminders).\
• Extract attendee names, emails, and the meeting title.
  
  Filtering — this is critical. Most contacts from email are noise. Use a two-stage filter:
  
  Stage 1 — Hard filters (always reject):\
• My own email addresses and domains.\
• Emails from family or personal contacts I've explicitly excluded (configurable list).\
• Contacts already in the CRM or previously rejected.\
• Generic role-based inboxes: info@, team@, partnerships@, collabs@, noreply@.\
• Marketing/transactional domains matching patterns like: noreply@, tx., cx., mail., email. prefixes.
  
  Stage 2 — AI classification (use a fast, cheap LLM like Gemini Flash or Haiku):
  Send the candidate's name, email, exchange count, and sample subject lines/snippets to an LLM with these rules:\
• REJECT clearly automated or notification-only senders.\
• REJECT if all sample subjects look like newsletters, digests, or automated reports ("weekly roundup", "monthly update", "AI news").\
• REJECT cold outreach with low engagement — if exchanges are low relative to total emails, it's one-way pitching.\
• REJECT if snippets show repetitive promotional content (product launches, feature announcements, affiliate reports).\
• APPROVE only if it looks like a real person with genuine two-way interaction or a meaningful business relationship.\
• Higher confidence for real back-and-forth conversations with varied, substantive topics.
  
  Contact scoring (used for ranking, not filtering):\
• Base score: 50\
• +5 per email exchange (max +20)\
• +3 per meeting (max +15)\
• +15 if their title matches preferred titles (CEO, Founder, VP, Head of, Engineer, Partner, etc.)\
• +10 if they appeared in small meetings (≤3 attendees)\
• +10 if last interaction was within 7 days, +5 if within 30 days\
• +25 bonus if the person appears in both email AND calendar (stronger signal)\
• +10 if they have a recognizable role, +5 if they have a company
  
  For each approved contact, store:\
• Name, email(s), company, role/context\
• Interaction timeline with dates\
• Last-touch timestamp (auto-updated)\
• Contact score\
• Tags or categories
  
  Learning system:\
• Maintain a learning.json config with:\
• skip_domains: domains to always reject (populated over time from rejections)\
• prefer_titles: titles that boost contact scores\
• skip_keywords: subject-line keywords that indicate spam\
• min_exchanges: minimum exchange threshold (default 1)\
• max_days_between: max days since last interaction (default 60)\
• max_attendees: meeting size cap (default 10)\
• min_duration_minutes: meeting length minimum (default 15)\
• When I reject a contact, learn from it — add their domain to skip_domains if appropriate.
  
  Deduplication:\
• When a new contact is found, check by email, then by name+company combination.\
• Merge records rather than creating duplicates.
  
  Semantic retrieval:\
• Generate embeddings for each contact record.\
• Let me ask natural-language questions like:\
• "Who did I meet from [company] last month?"\
• "When did I last talk to [name]?"\
• "Show contacts I haven't spoken to in 30+ days."
  
  Storage: SQLite with WAL mode and foreign keys enabled.
  
  Notifications: After each ingestion run, send a summary of new contacts, merges, rejections, and any issues.\

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---\
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## 2) Knowledge Base (RAG) — Save Anything, Recall It Later
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```\
Build me a personal knowledge base with RAG (retrieval-augmented generation).\
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Ingestion — I send a URL or file and the system saves it. It should handle:\
- Web articles\
- YouTube videos (transcripts)\
- Tweets/X posts\
- PDFs\
- Plain text or notes\
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Source type detection: Determine the type from the URL pattern or file extension. Classify as: article, video, pdf, text, tweet, or other.\
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Content extraction with fallback chain — this is important because no single extractor works for every site:\
1. For Twitter/X URLs:\
a. Try FxTwitter API (api.fxtwitter.com) — free, no auth needed\
b. Fall back to X API direct lookup\
c. Fall back to web scraping\
2. For YouTube URLs:\
a. Pull transcript via YouTube transcript API or yt-dlp\
3. For all other URLs (articles, blogs, etc.):\
a. Try a clean text extractor (like Mozilla Readability or similar)\
b. Fall back to Firecrawl or Apify for sites that block simple extraction\
c. Fall back to a headless browser (Playwright/Puppeteer) for JavaScript-heavy pages\
d. Last resort: raw HTTP fetch + HTML tag stripping\
- Retry once on transient errors (ECONNRESET, ETIMEDOUT, DNS failures) with a 2-second delay.\
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Content quality validation — reject bad extractions:\
- Minimum 20 characters of content.\
- For articles/non-tweets: at least 15% of non-empty lines must be longer than 80 characters (to detect prose vs. navigation menus).\
- Total content must be at least 500 characters for non-tweet types.\
- Detect error pages by looking for 2+ signals: "access denied", "captcha", "please enable javascript", "cloudflare", "404", "sign in", "blocked", "rate limit".\
- Maximum content length: 200,000 characters (truncate beyond this).\
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Deduplication — two layers:\
1. URL-based: Normalize URLs before comparing — strip tracking params (utm_source, utm_medium, utm_campaign, fbclid, igshid, ref, s, t), remove www., normalize twitter.com to x.com, remove trailing slashes and fragments.\
2. Content-hash: SHA-256 hash of the cleaned content. Store as a UNIQUE column — reject if the same hash already exists.\
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Chunking:\
- Chunk size: 800 characters per chunk.\
- Overlap: 200 characters between chunks.\
- Minimum chunk size: 100 characters (append tiny remainders to the last chunk).\
- Split on sentence boundaries (regex: /(?<=[.!?])\\s+/).\
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Embedding generation:\
- Use Google gemini-embedding-001 (768 dimensions, free) or OpenAI text-embedding-3-small (1536 dimensions) as fallback.\
- Max input: 8000 characters per chunk.\
- Process in batches of 10 chunks with 200ms delay between batches.\
- Retry failed embeddings 3 times with exponential backoff (1s, 2s, 4s).\
- Cache embeddings with an LRU cache (1000 entries).\
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Storage — SQLite with two tables:\
- sources: id, url, title, source_type, summary, raw_content, content_hash (UNIQUE), tags (JSON array), created_at, updated_at\
- chunks: id, source_id (FK), chunk_index, content, embedding (BLOB), embedding_dim, embedding_provider, embedding_model, created_at\
- Index on chunks(source_id), sources(source_type), sources(content_hash).\
- Enable WAL mode and foreign keys with CASCADE deletes.\
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Concurrency protection: Use a lock file to prevent simultaneous ingestion runs. Check if lock is stale (PID dead or file older than 15 minutes).\
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Retrieval — When I ask a question:\
1. Embed my query using the same embedding provider.\
2. Cosine similarity search over all stored chunks. Return top 10.\
3. Deduplicate results: keep only the best chunk per source.\
4. Sanitize content in results (max 2500 characters per excerpt).\
5. Pass top chunks to an LLM: "Answer using only the provided context. Cite which sources you drew from."\
6. Return the answer with source references.\
```\
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---\
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## 3) Content Idea Pipeline (Research → Dedupe → Project Management)
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```\
Build a content idea pipeline that researches topics, prevents duplicate ideas, and creates production-ready tasks.\
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Trigger: I describe a topic idea in chat.\
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Step 1 — Research:\
- Search X/Twitter for recent discourse on the topic (use the tiered approach from Use Case #4 if built).\
- Search my knowledge base (Use Case #2) for related saved content.\
- Optionally run a web search.\
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Step 2 — Semantic dedupe (this is the critical gate):\
- Maintain a database of all past content ideas with these fields:\
- id (format: YYYY-MM-DD-NNN)\
- date, type (short/long-form), title, slug (URL-friendly, UNIQUE)\
- summary (the pitch text)\
- tags (comma-separated)\
- status: pitched, accepted, rejected, produced, duplicate\
- response (my feedback)\
- embedding (BLOB — generated via Gemini or OpenAI)\
- created_at\
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- When a new idea comes in, run a hybrid similarity search:\
- Semantic similarity (70% weight): Cosine similarity between the new idea's embedding and all stored embeddings.\
- Keyword matching (30% weight): Match against title (30%), summary (20%), and tags (20%).\
- Combined score: (semantic × 0.7) + (keyword × 0.3)\
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- Hard gate: If any existing idea scores above 40% combined similarity, REJECT the new idea. Show me what it matched and the similarity score. No exceptions — this prevents re-pitching old concepts.\
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Step 3 — Brief assembly (only if it passes dedupe):\
- Keep it short — a few sentences on the angle and why it's video-worthy.\
- Do NOT generate titles, thumbnails, hooks, or scripts unless I explicitly ask.\
- Include links to relevant tweets or KB sources from the research step.\
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Step 4 — Create task:\
- Create a task in my project management tool (Asana, Linear, Notion, or Todoist) with the brief as the description.\
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Step 5 — Store the idea:\
- Add the new idea + its embedding to the pitch database so future ideas are compared against it.\
- Set initial status to "pitched".\
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Step 6 — Notify me with the brief and a link to the created task.\
```\
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---\
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## 4) Social Media Research System (Cost-Optimized)
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```\
Build a social media research tool that answers "What are people saying about [topic]?" using X/Twitter, while minimizing API costs.\
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Query decomposition:\
- Take my question and break it into 2-4 focused search queries covering different angles.\
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Tiered retrieval — always try the cheapest option first:\
- Tier 1 (free): FxTwitter API (api.fxtwitter.com) — works for individual tweet lookups, no auth needed.\
- Tier 2 (low-cost, ~$0.15/1K tweets): A paid Twitter data provider like TwitterAPI.io or SocialData. Supports search, profiles, user tweets, and thread context.\
- Tier 3 (expensive, ~$0.005/tweet): Official X API v2 (api.x.com/2). Last resort only. Rate limit to 350ms between requests to stay under 450 req/15min.\
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How the tiers cascade by operation:\
- Single tweet lookup: Tier 1 → Tier 2 → Tier 3\
- Search: Tier 2 → Tier 3\
- Profile lookup: Tier 2 → Tier 3\
- Thread expansion: Tier 2 → Tier 3\
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Filtering:\
- Filter by timeframe (default: last 7 days, configurable).\
- Rank by engagement (likes + retweets + replies).\
- Remove retweets and duplicate content.\
- Suppress low-quality or spam results.\
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Thread expansion: For high-engagement tweets that are part of a thread, pull the full thread.\
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Caching: Cache results with a 1-hour TTL so repeated queries don't burn API credits.\
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Usage logging: Log every API call to a separate log file per tier, with timestamps and estimated costs.\
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Output — synthesize into a briefing:\
- Key narratives: 3-5 dominant takes\
- Notable posts: 5-10 most relevant tweets with links\
- Sentiment summary: positive, negative, or mixed\
- Contrarian takes: interesting minority opinions\
```\
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---\
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## 5) YouTube Analytics + Competitor Tracking
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```\
Build a YouTube analytics system that tracks my channel daily and monitors competitors.\
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My channel — collect these metrics daily via YouTube Data API and YouTube Analytics API:\
- views (total and per-video)\
- estimatedMinutesWatched (watch time)\
- averageViewDuration\
- subscribersGained\
- impressions and CTR (if available via Analytics API)\
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Store each day's metrics in a SQLite table:\
- daily_stats: video_id, date, views, watch_time_minutes, avg_view_duration, impressions, ctr, subscribers_gained\
- videos: id, title, published_at, thumbnail_url, duration_seconds\
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Derived metrics:\
- 7-day moving average for daily views\
- Subscriber-to-view conversion rate\
- Views-per-video trend\
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Competitor tracking:\
- Accept a configurable list of competitor channel IDs/usernames.\
- Daily, pull each competitor's recent uploads (title, publish date, views) and subscriber count via YouTube Data API.\
- Track upload cadence and momentum.\
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Chart generation — produce PNG charts using matplotlib (dark theme):\
1. "trend" chart: Daily view trend for a specific video (line chart with fill area)\
2. "top" chart: Top N videos by views (horizontal bar chart, color-coded by short vs long-form)\
3. "daily" chart: Channel-wide daily views with 7-day moving average overlay\
- Format numbers with commas, dates as readable labels, include grid and legends.\
- Save charts to a designated output folder.\
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Schedule: Daily cron job collecting all data. Optional daily summary sent to chat.\
```\
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---\
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## 6) Nightly Business Briefing (Multi-Perspective AI Council)
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```\
Build a nightly business analysis system that collects signals from across my tools and produces prioritized recommendations using a multi-persona AI review council.\
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Signal collection — pull data from whatever systems you use. Normalize each signal into:\
{ source, signal_name, value, confidence (0-100), direction (up/down/flat), category }\
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Example sources: YouTube metrics, CRM health, project management backlog, social growth, email themes, meeting action items, sales pipeline, operations/cron reliability.\
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Compact to the top 200 signals by confidence score, with up to 4 sample events per source.\
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Three-phase AI review council (all phases use a frontier model like Claude Opus or GPT-4):\
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Phase 1 — Draft (LeadAnalyst):\
- Score business outlook on weekly and monthly horizons (0-100), plus a blended average.\
- Generate 5-10 recommendations, each with: title, description (2-3 plain sentences), evidence references, estimated impact (0-100), estimated effort (0-100), confidence (0-100).\
- Constraints: use only provided data, no made-up facts, plain human language, don't recommend publishing content immediately.\
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Phase 2 — Parallel review (4 personas, run simultaneously via Promise.all):\
1. GrowthStrategist: Scalable growth, asymmetric upside. Demands concrete evidence.\
2. RevenueGuardian: Near-term revenue and cash flow. Challenges anything that doesn't protect income.\
3. SkepticalOperator: Execution reality, data quality risks. Challenges weak assumptions.\
4. TeamDynamicsArchitect: Team health, collaboration quality, sustainable pace.\
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Each reviewer:\
- Critiques every recommendation: support, revise, or reject (with score adjustments)\
- Can propose new recommendations the draft missed\
- Returns: reviewer_name, key_findings[], votes[] (with adjusted scores), new_recommendations[]\
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Phase 3 — Consensus (CouncilModerator):\
- Receives draft + all 4 reviews.\
- Reconciles disagreements.\
- Produces final recommendation set with consensus notes.\
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Fallback: If consensus fails, use draft recommendations. If a reviewer fails, substitute a stub review with the error message.\
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Ranking — score each recommendation:\
priority = (impact × w1) + (confidence × w2) + ((100 - effort) × w3)\
Default weights: w1=0.4, w2=0.35, w3=0.25. Store weights in a policy table and update via feedback over time.\
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Filter: Remove any "publish now" recommendations (hard constraint).\
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Output: Deliver ranked recommendations to my preferred channel. Store full council trace (draft, all reviews, consensus, models used) in a database for auditing and backtesting.\
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Schedule: Run nightly via cron.\
```\
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---\
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## 7) CRM/Business Tool Natural Language Access
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```\
Build a natural-language interface to my CRM (HubSpot, Salesforce, or whatever I use).\
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Supported objects and operations:\
- Contacts: search, create, update, list, get by ID\
- Companies: search, create, update, list\
- Deals: search, create, update, list, query by pipeline stage\
- Owners: list\
- Associations: link contacts to companies, deals to contacts, deals to companies\
(For HubSpot, common association type IDs: 1=Contact→Company, 3=Deal→Contact, 5=Deal→Company)\
- Properties/schema: inspect available fields for any object\
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Intent classification — parse my message into:\
1. Lookup: "Find the contact for [name]" → search contacts\
2. Create: "Add a new contact: [name], [email], [company]" → create contact\
3. Update: "Update [contact]'s title to [new title]" → update contact\
4. List: "Show all deals in negotiation stage" → filter deals\
5. Associate: "Link [contact] to [company]" → create association\
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Validation: If I'm missing required fields, ask me before proceeding.\
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Response format: Return clean, readable summaries — not raw JSON. For deal queries, include: deal name, amount, stage, close date, last modified.\
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Authentication: API key or OAuth stored in environment variables.\
```\
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---\
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## 8) AI Content Humanization
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```\
Build a text rewriting tool that removes AI-generated artifacts and makes content sound like a real person wrote it.\
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Input: I paste draft text (social posts, blog paragraphs, emails, video scripts).\
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Step 1 — Detection. Scan for common AI tells:\
- Overuse of: "delve", "landscape", "leverage", "it's important to note", "in conclusion", "game-changing", "revolutionary", "transformative"\
- Tone inflation: dramatic language when the subject doesn't warrant it\
- Generic phrasing that could apply to any topic\
- Repetitive sentence structures (every sentence starting the same way)\
- Excessive hedging: "It's worth noting that perhaps..."\
- Lists that feel generated (too clean, too parallel, no personality)\
- Identical paragraph lengths and rhythms\
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Step 2 — Rewrite:\
- Replace vague qualifiers with specific, concrete language\
- Vary sentence length (mix short punchy sentences with longer ones)\
- Use contractions, sentence fragments, and informal word choices where natural\
- Remove filler while keeping the core message\
- Add the kind of imperfections real writing has — not errors, but human cadence\
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Step 3 — Channel tuning (optional, based on destination):\
- Twitter/X: Punchy, under 280 chars, direct, no filler\
- LinkedIn: Professional but conversational, authoritative without being stiff\
- Blog: Longer form, personal anecdotes and opinions welcome\
- Email: Brief, clear, action-oriented, warm but efficient\
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Output: Return the revised text. Optionally highlight what changed and why.\
```\
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---\
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## 9) Image Generation + Iterative Editing
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```\
Build an image generation workflow where I describe what I need in chat and iterate until it's right.\
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Core flow:\
1. I describe an image: subject, style, composition, mood, colors, any text to include.\
2. Generate 1-3 variants using an image generation API (DALL-E 3, Midjourney API, Stable Diffusion, or Flux).\
3. I review and either:\
a. Accept → save as final asset\
b. Request changes → I describe adjustments ("darker background", "remove text", "more minimal") and it generates new variants incorporating my feedback\
4. Loop continues until I'm satisfied.\
\
Image editing:\
- I can send an existing image and ask for edits: swap background, adjust composition, combine multiple images.\
- Use inpainting or img2img when available.\
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Context tracking: Remember what we've been working on across multiple messages in the same session so I don't have to re-describe the whole concept each iteration.\
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Output: Save final assets to a designated folder. Deliver as downloadable files in chat.\
```\
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---\
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## 10) Task Management from Meetings + Chat
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```\
Build a system that extracts action items from meetings and conversations, gets my approval, then creates tasks in my task manager.\
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Input sources:\
- Meeting transcripts from Fathom, Otter.ai, Fireflies, or any transcript provider\
- Notes I paste into chat\
- Direct commands: "Add a task to follow up with [person] by Friday"\
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Extraction — use an LLM (Gemini Flash or similar fast model) to parse transcripts and extract:\
- description: what needs to be done (max 150 characters, clear and actionable)\
- assignee: who is responsible (name as it appears in the transcript)\
- is_owner: boolean — is this MY action item or someone else's?\
- todoist_title: clean task title (max 120 chars) — only generate this for items where is_owner is true\
- Do NOT extract vague items like "think about X" — only concrete actions with a clear next step.\
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CRM cross-reference (if you have Use Case #1 built):\
- Match mentioned people against CRM contacts to enrich with company/role context.\
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Approval flow — this is important, don't auto-create tasks:\
1. Show me the extracted task list with each item numbered.\
2. Let me choose: "all", "none", or specific items by number (e.g. "1, 3, 5").\
3. Let me edit any task before confirming (change title, priority, due date).\
4. Only after I approve, create the tasks via the Todoist/Asana/Linear API.\
5. If any task creation fails, keep it in a pending state for retry.\
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Direct task creation:\
- When I say "remind me to [thing] by [date]", skip extraction and create directly after confirming.\
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Output: Send confirmation with links to created tasks. Persist approval state so I can check what's pending.\
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Default project: Configure a default project/list for tasks (e.g. "Work" or my company name).\
```\
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---\
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## 11) AI Usage and Cost Tracking
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```\
Build a logging and reporting system that tracks every AI API call and provides cost visibility.\
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Logging — wrap every LLM/AI API call with a logger that records this structure (one JSON object per line in a JSONL file):\
{\
"timestamp": "ISO8601",\
"model": "model-name",\
"tokens": { "input": number, "output": number, "total": number },\
"taskType": "string (e.g. crm-ingestion, x-research, image-gen, business-analysis)",\
"description": "string (brief note on what this call was for)",\
"costEstimate": number,\
"source": "tracker-name"\
}\
\
Cost calculation — use a pricing lookup table (per 1M tokens, input/output):\
- Anthropic: Opus ($15/$75), Sonnet ($3/$15), Haiku ($0.80/$4)\
- OpenAI: GPT-4 ($30/$60), GPT-4 Turbo ($10/$30), GPT-3.5 Turbo ($0.50/$1.50), o1 ($15/$60)\
- Google: Gemini Pro ($10/$30), Gemini Flash ($0.30/$1.20), Gemini 1.5 Pro ($1.25/$5)\
- xAI: Grok ($2/$10)\
- Default for unknown models: $1/$3\
- Formula: (inputTokens / 1M) × inputPrice + (outputTokens / 1M) × outputPrice\
- Keep this table in a config file so it's easy to update when pricing changes.\
\
Storage: Append-only JSONL file. Optionally mirror to SQLite for faster querying.\
\
Reporting — generate on demand with filters (--days N, --model X, --task-type Y):\
1. Overall summary: total calls, total tokens (input/output/total), total cost\
2. By model: calls, tokens, cost — sorted by cost descending\
3. By task type: calls, tokens, cost — sorted by cost descending\
4. By day: last 10 days showing calls, tokens, and cost per day\
5. Trend: daily or weekly spending over the last 30/90 days\
\
Routing suggestions:\
- Flag any task type where a frontier model (Opus, GPT-4) is being used but the task is simple (short inputs, formatting-only, data extraction) — suggest downgrading to a cheaper model.\
- Flag workflows that account for >25% of total spend as optimization candidates.\
- Suggest caching strategies for repeated queries (especially research workflows).\
\
Output: Markdown report or simple web dashboard. Optionally send weekly cost summary to my chat.\
Here are focused, ready-to-run prompts you can paste into an AI coding assistant to implement each OpenClaw use case as a concrete code project (TypeScript or Python). Each is scoped to one use case and assumes a local, self-hosted stack (SQLite, cron, env-based secrets).
You can ask me next: “Refine #N for Python FastAPI + SQLite-vec” or “Refine #N for Node/TypeScript + Prisma” and we’ll harden one into a full spec.

Personal CRM Intelligence — Implementation Prompt
You are a senior backend engineer.Implement a local-first personal CRM ingestion + scoring service with the following requirements:
Tech stack and basics:
Use either: (a) Python 3.11 + FastAPI + SQLAlchemy, or (b) Node 20 + TypeScript + Express + Prisma.
Persistence: SQLite database file personal_crm.db with WAL mode and foreign keys enabled.
All secrets (Gmail, Google Calendar, LLM API keys) from environment variables.
Provide a minimal CLI wrapper (e.g. python -m app ingest or node cli.js ingest) for cron.
Data sources and ingestion:

Email:
  * Integrate with Gmail via Gmail API or IMAP (configurable via env).

  * Scan the last 60 days of messages.

  * Extract: thread id, message id, date, sender, recipients, subject, snippet/body preview.

  * For each contact candidate, compute:

    * total_messages in threads involving that email.

    * thread_count involving that email.

    * exchange_count = Math.min(Math.floor(total_messages / 2), thread_count).

Calendar:
  * Integrate with Google Calendar API.

  * Scan the last 60 days of events.

  * Only include meetings with 1–10 attendees and duration ≥ 15 minutes.

  * Extract: start/end times, attendees (name + email), title.

Filtering (two-stage):
Stage 1 — hard filters:

Maintain learning.json config file with:
  * skip_domains: string[]

  * prefer_titles: string[]

  * skip_keywords: string[]

  * min_exchanges: number (default 1)

  * max_days_between: number (default 60)

  * max_attendees: number (default 10)

  * min_duration_minutes: number (default 15)

Always reject:
  * My own email addresses/domains (config: own_emails[], own_domains[]).

  * Emails in an explicit skip_contacts[] list.

  * Contacts already in CRM or previously rejected.

  * Generic inboxes: addresses starting with info@, team@, partnerships@, collabs@, noreply@.

  * Domains or local parts matching marketing patterns: prefixes noreply., tx., cx., mail., email..

  * Contacts with exchange_count < learning.min_exchanges.

Stage 2 — LLM-based classification:

For each remaining candidate, call a cheap LLM (Gemini Flash / Claude Haiku / GPT-4o-mini is fine; make the provider pluggable).
Prompt input fields:
  * name

  * email

  * exchange_count

  * list of sample subject lines (up to 5)

  * short snippets/body previews (up to 5)

LLM must respond with strict JSON: { "decision": "APPROVE" | "REJECT", "reason": "string" }.
Apply rules:
  * REJECT clearly automated or notification-only senders.

  * REJECT if all sample subjects look like newsletters/digests/reports.

  * REJECT cold outreach with low engagement (many one-way promos, low replies).

  * REJECT repetitive promo content (product launches, features, affiliate reports).

  * APPROVE only for genuine, two-way or meaningful relationships.

Treat malformed JSON from the LLM as REJECT with logged warning.
Contact model, scoring, and dedupe:
Database schema (simplified, adjust types to ORM):

contacts:
  * id (PK)

  * name

  * primary_email

  * emails (JSON array of strings)

  * company

  * role

  * context (short free-text)

  * last_touch_at (datetime)

  * contact_score (integer)

  * source_flags (JSON: { email: boolean, calendar: boolean })

  * tags (JSON array of strings)

  * created_at, updated_at

interactions:
  * id (PK)

  * contact_id (FK → contacts.id)

  * kind ("email" | "meeting")

  * external_id (e.g. Gmail thread ID or Calendar event ID)

  * occurred_at (datetime)

  * metadata (JSON)

rejections:
  * id (PK)

  * email

  * reason

  * domain

  * llm_reason

  * created_at

Add indexes on contacts.primary_email, contacts.name, and interactions.contact_id.
Deduplication rules:

When adding or updating a contact:
  * First search for an existing contact by exact primary_email.

  * If not found, search by any email in emails array.

  * If still not found, search by name + company combination (case-insensitive).

  * Merge records rather than creating duplicates:

    * Union of email sets.

    * Update last_touch_at to max of all.

    * Merge tags and source flags.

For rejections, track domain for each entry; when I explicitly reject a contact later (via some admin UI or CLI), add their domain to learning.skip_domains if it’s not my own domain.
Contact scoring (at each ingestion run, recompute):

Base score: 50.
+5 per email exchange (max +20).
+3 per meeting (max +15).
+15 if title matches learning.prefer_titles (e.g., "CEO", "Founder", "VP", "Head of", "Engineer", "Partner" — seeded defaults).
+10 if they appeared in small meetings (≤3 attendees) at least once.
Recency bonus:
  * +10 if last_touch_at within 7 days.

  * +5 if within 30 days.

+25 if person appears in both email and calendar.
+10 if they have a recognizable role (non-empty) and +5 if company is non-empty.
Semantic retrieval:

Create an embeddings table:
  * contact_embeddings:

    * contact_id (PK, FK → contacts.id)

    * embedding (BLOB)

    * embedding_dim (int)

    * provider (e.g. "google", "openai")

    * model

    * updated_at

Generate one embedding per contact from a concatenation of:
  * name, emails, company, role, top interaction subjects, and recent interaction snippets; max ~1000 chars.

Use either:
  * A vector extension (sqlite-vec) for cosine similarity, or

  * Load all embeddings into memory and compute cosine similarity in app code for now.

Add a retrieval function/API that lets me ask:
  * “Who did I meet from [company] last month?”

  * “When did I last talk to [name]?”

  * “Show contacts I haven’t spoken to in 30+ days.”

Implement these via a mix of:
  * Semantic similarity over embeddings for free-text queries.

  * Simple SQL filters on interactions and contacts.last_touch_at.

Notifications:

After each ingestion run (invoked via CLI), compute:
  * new_contacts_count

  * merged_contacts_count

  * rejected_contacts_count

  * any errors

Print a human-readable summary to stdout and optionally send a webhook or email summary (add a simple notifier interface).
Log detailed ingestion stats to a JSONL log file.
Deliverables:

Fully working service with:
  * CLI commands:

    * ingest-email

    * ingest-calendar

    * ingest-all (both + scoring + notifications)

  * HTTP endpoints (optional but preferred):

    * GET /contacts?query=... (search by text, uses semantic + basic filters)

    * GET /contacts/:id

    * POST /contacts/:id/reject (updates learning.json when appropriate)

Include a short README.md with:
  * Setup instructions,

  * How to configure Gmail/Calendar,

  * Example cron line for daily ingestion.

Use best practices: retry transient API failures, structured logging, and clear separation of providers (email/calendar/LLM) from the domain logic. Use the OpenClaw/OpenAI/Gemini LLM examples and SQLite+vector patterns from SQLite RAG guides as inspiration, but write all code from scratch.
2) Personal Knowledge Base (RAG on SQLite) — Implementation Prompt
You are a backend/infra engineer.
Implement a personal RAG knowledge base with URL/file ingestion, SQLite storage, and embedding-based retrieval:
Tech & storage:

Language: Python 3.11 preferred (FastAPI + Pydantic + SQLAlchemy), but Node 20 + TS is acceptable.
Database: SQLite kb.db with WAL mode and foreign keys.
Two tables:
  * sources(id, url, title, source_type, summary, raw_content, content_hash UNIQUE, tags JSON, created_at, updated_at)

  * chunks(id, source_id FK, chunk_index, content, embedding BLOB, embedding_dim, embedding_provider, embedding_model, created_at)

Indexes:
  * CREATE INDEX idx_chunks_source_id ON chunks(source_id);

  * CREATE INDEX idx_sources_source_type ON sources(source_type);

  * CREATE UNIQUE INDEX idx_sources_content_hash ON sources(content_hash);

Source type detection:

Accept:
  * URLs (web articles, YouTube, Twitter/X, other)

  * Files: PDFs, text, markdown.

Classify type by pattern:
  * Twitter/X: twitter.com, x.com.

  * YouTube: youtube.com, youtu.be.

  * PDFs by .pdf extension.

  * Else: article or text depending on input.

Content extraction (fallback chain):
For Twitter/X URLs:

Try FxTwitter API<a href="https://api.fxtwitter.com" target="_blank" rel="noopener noreferrer nofollow"></a> for tweet text, author, metadata (no auth).​
If that fails, fall back to official X API (stub provider with env-based token).
If that fails, fall back to HTTP fetch + HTML parsing.
For YouTube URLs:

Try YouTube transcript extraction via:
   * Either official YouTube Data API, or

   * yt-dlp or similar library to fetch transcript/captions.

Extract: title, transcript text, (optionally) chapters.
For all other URLs:

Try a “readability” style extractor (e.g., Mozilla Readability port, or trafilatura/readability-lxml in Python).
If content is too short or looks like boilerplate, fall back to:
   * Firecrawl/Apify API (configure via env) OR a generic scraping provider.

If still failing or JS-heavy, fall back to headless browser (Playwright/Puppeteer) and re-run readability on rendered HTML.
Last resort: raw HTTP fetch + HTML tag stripping.
For files:

PDFs: use a robust PDF text extractor (e.g. pdfminer.six, pymupdf, or pdfplumber).
Plain text / markdown: treat file content as raw_content.
Retries:

For network calls, retry once on transient errors (ECONNRESET, ETIMEDOUT, DNS failures) with ~2-second delay.
Content quality validation:

Reject ingestion if:
  * Total content < 20 characters.

  * For non-tweets:

    * Total content < 500 characters.

    * Or < 15% of non-empty lines are longer than 80 characters (indicates mostly nav/boilerplate).

  * If page text contains at least 2 signals of error: "access denied", "captcha", "please enable javascript", "cloudflare", "404", "sign in", "blocked", "rate limit".

Truncate any content beyond 200,000 characters.
Deduplication:

URL normalization:
   * Strip tracking params: utm_*, fbclid, igshid, ref, s, t.

   * Normalize www..

   * Normalize twitter.com → x.com.

   * Remove trailing slashes and fragments.

Content hash:
   * Compute SHA-256 of cleaned content.

   * Store as content_hash and enforce UNIQUE.

   * If a source with same hash exists, skip creating a duplicate.

Chunking:

Chunk size: 800 characters.
Overlap: 200 characters between chunks.
Minimum chunk size: 100 characters; append small remainders to previous chunk.
Split on sentence boundaries using regex (?<=[.!?])\s+.
Store chunk_index starting at 0.
Embedding generation:

Use:
  * Google gemini-embedding-001 (768-dim) as primary, or

  * OpenAI text-embedding-3-small (1536-dim) as fallback.

Max input per chunk: 8000 characters (well above our chunk size).
Process in batches of 10 chunks with 200ms delay between batches to avoid rate limits.
Retry failed embedding calls up to 3 times with exponential backoff (1s, 2s, 4s).
Implement an in-memory LRU cache (size 1000) keyed by (provider, model, content_hash) to avoid re-embedding identical content.
Concurrency protection:

Use a lockfile (e.g. .kb_ingest.lock):
  * On ingestion start, create lock with PID and timestamp.

  * If lock exists:

    * If PID alive and lock age < 15 minutes → abort new ingestion.

    * If PID dead or lock age ≥ 15 minutes → treat as stale and override.

Retrieval:

HTTP endpoint: POST /query with { "question": string, "max_sources": number }.
Steps:
  1. Embed query with same provider/model as chunks.

  2. Run cosine similarity search over all chunk embeddings:

     * Either with SQLite-vec / vector extension, or in-memory compute.

     * Return top 10 chunks.

  3. Deduplicate by source_id: keep best chunk per source.

  4. For each selected chunk, sanitize content to max 2500 characters (center around high-density parts if you want; simple truncation is ok).

  5. Call an LLM (configurable, e.g. local via OpenAI-compatible endpoint) with system prompt:

     * “You are a helpful assistant. Answer the user’s question using only the provided context chunks. If the answer is not in the context, say you don’t know. For each assertion, cite the source id(s) used.”

  6. Return:

     * answer (string)

     * sources array with:

       * source_id

       * url

       * title

       * source_type

       * excerpt (chunk content substring)

CLI/HTTP:

Provide:
  * CLI: ingest-url <url>, ingest-file <path>, query "<question>".

  * HTTP: POST /ingest/url, POST /ingest/file (multipart), POST /query.

Draw from SQLite RAG implementations that use SQLite-vec / sqlite-ai, but implement your own simplified layer around SQLite for storing BLOB embeddings and doing cosine similarity in app code if the vector extension is not used.​
3) Content Idea Pipeline — Implementation Prompt
You are implementing a content idea dedupe + task creation pipeline.
Core requirements:

Language: Python 3.11 or Node 20 + TS.
Storage: SQLite content_ideas.db.
External systems (optional adapters):
  * X/Twitter search via some data provider.

  * Personal KB (Use Case #2) via HTTP API.

  * Project management tool (Asana / Linear / Notion / Todoist) via env-configured adapter.

Schema:

ideas:
  * id (TEXT, PK, format YYYY-MM-DD-NNN)

  * date (DATE)

  * type ("short" | "long")

  * title (TEXT)

  * slug (TEXT, UNIQUE, URL-friendly; kebab-case from title or short pitch)

  * summary (TEXT) — the pitch text user provides (plus minimal system edits).

  * tags (TEXT, comma-separated)

  * status ("pitched" | "accepted" | "rejected" | "produced" | "duplicate")

  * response (TEXT) — my feedback or notes.

  * embedding (BLOB)

  * embedding_dim (INT)

  * created_at (DATETIME)

Add index on status and date.
Trigger / API:

When user submits a new idea via chat or HTTP:
  * Input: { idea_text: string, type?: "short" | "long", tags?: string[] }.

  * Convert to canonical summary (light rewrite allowed).

  * Generate a default title if not provided (short but descriptive).

  * Generate a slug (lowercase, hyphenated, dedup by appending -2, -3, etc. if needed).

Step 1 — Research (optional but wired for future):

Given idea_text, run:
  * Twitter/X search (if configured): recent tweets, last ~7–30 days.

  * Internal KB search (Use Case #2) via HTTP query.

  * Optional web search via any provider.

Store research refs (URL + snippet) in a transient structure for the brief, not in DB (or in a side table if needed).
Step 2 — Semantic dedupe (critical gate):

Compute an embedding for idea_text using Gemini/OpenAI.
For all existing ideas:
  * Semantic similarity:

    * Cosine similarity between new idea’s embedding and each stored embedding.

    * Normalize semantic score to.

  * Keyword matching:

    * Tokenize new idea’s words (simple lowercased terms, remove stopwords).

    * For each existing idea:

      * Title keyword overlap weight 0.3.

      * Summary overlap weight 0.2.

      * Tag overlap weight 0.2.

    * Aggregate keyword score in.

  * Combined score:

    * combined = semantic * 0.7 + keyword * 0.3.

Hard rule:
  * If any existing idea has combined >= 0.4, reject the new idea as duplicate:

    * Return a response containing:

      * duplicate: true

      * matched_idea_id

      * matched_title

      * matched_summary

      * similarity: combined

    * Do not create a new DB record; instead, log this attempt if desired.

Step 3 — Brief assembly (if passes dedupe):

Generate a short brief:
  * 2–4 sentences max.

  * Focus on:

    * Angle and why it’s worth producing.

    * Target format (short vs long).

    * Concrete audience benefit.

  * Include relevant links from:

    * Tweets found in research.

    * KB sources found in research.

Do not generate titles, hooks, thumbnails, or script unless user explicitly asks.
Step 4 — Task creation:

Implement a TaskManager abstraction with drivers:
  * AsanaTaskManager, LinearTaskManager, NotionTaskManager, TodoistTaskManager.

Use env var TASK_MANAGER_PROVIDER to pick one.
Create a task with:
  * title: idea title.

  * description: generated brief plus research links.

  * tags/labels: from tags.

Return the created task URL or ID.
Step 5 — Store idea:

Generate an ID of the form YYYY-MM-DD-NNN:
  * Where YYYY-MM-DD is today.

  * NNN is a 3-digit sequence for the day (001, 002, …).

Insert into ideas table with:
  * status = "pitched".

  * embedding and embedding_dim.

Handle uniqueness constraints for slug and id.
Step 6 — Notify:

Respond to the calling client with:
  * duplicate: false

  * idea_id

  * title

  * brief

  * task_link

Deliverables:

Minimal REST API:
  * POST /ideas → dedupe, brief, task creation, store idea.

  * GET /ideas/:id

  * GET /ideas?status=...

LLM and embedding providers pluggable via config.
Unit tests for the dedupe function with synthetic examples at similarity <0.4, around 0.4, >0.4 to validate behavior.
Base the similarity + embedding logic on established RAG/SQLite vector patterns as in the RAG + SQLite material and adapt to this idea-table schema.
11) AI Usage and Cost Tracking — Implementation Prompt
You are implementing a centralized AI usage logger + cost reporter.
Requirements:
Core design:

Provide a small library (Python or Node) that wraps any LLM API call.
For each call, log a JSON object to an append-only JSONL file ai_usage.log.
Provide a small CLI (report) that reads the JSONL (and optional SQLite mirror) and prints cost summaries.
Logging structure:

Each log line is a single JSON object:
   &nbsp;&nbsp;json &nbsp;&nbsp;

  { "timestamp": "ISO8601", "model": "model-name", "tokens": { "input": number, "output": number, "total": number }, "taskType": "string", "description": "string", "costEstimate": number, "source": "tracker-name" }

timestamp: UTC ISO8601.
taskType: e.g., "crm-ingestion", "x-research", "image-gen", "business-analysis".
source: arbitrary string identifying which wrapper produced this (e.g., "backend-api", "cli-tool").
Cost calculation:

Maintain a config file pricing.json mapping model families to prices per 1M tokens:
  * Anthropic:

    * Opus: input 15, output 75

    * Sonnet: input 3, output 15

    * Haiku: input 0.8, output 4

  * OpenAI:

    * GPT-4: 30 / 60

    * GPT-4 Turbo: 10 / 30

    * GPT-3.5 Turbo: 0.5 / 1.5

    * o1: 15 / 60

  * Google:

    * Gemini Pro: 10 / 30

    * Gemini Flash: 0.3 / 1.2

    * Gemini 1.5 Pro: 1.25 / 5

  * xAI:

    * Grok: 2 / 10

  * Default: 1 / 3

Implement a function:
  * estimateCost(modelName, inputTokens, outputTokens):

    * Normalizes modelName to family (e.g. "gpt-4o-mini" → "gpt-4", "claude-3-opus" → "opus").

    * Uses pricing.json to compute:

      * (inputTokens / 1_000_000) * inputPrice + (outputTokens / 1_000_000) * outputPrice.

Storage:

Always append to ai_usage.log (JSONL).
Optionally mirror to SQLite ai_usage.db:
  * logs(id, timestamp, model, input_tokens, output_tokens, total_tokens, task_type, description, cost_estimate, source).

  * Provide a flag/env to turn SQLite mirroring on or off.

Reporting CLI:

Command: ai-cost report [--days N] [--model X] [--task-type Y].
Reads JSONL (and optionally SQLite for speed).
Implement filters:
  * --days N: only include events from last N days.

  * --model X: filter by model substring.

  * --task-type Y: filter by exact or prefix match.

Produce reports:
  1. Overall summary:

     * Total calls, total tokens (input/output/total), total cost.

  2. By model:

     * Table with: model, calls, input_tokens, output_tokens, total_tokens, cost.

     * Sorted by cost descending.

  3. By task type:

     * Same fields, grouped by taskType, sorted by cost.

  4. By day:

     * Last 10 days, each with date, calls, tokens, cost.

  5. Trend:

     * Over last 30 and 90 days, compute daily or weekly totals; print as simple text table.

Routing suggestions:

In the report, compute:
  * For each taskType:

    * Share of total cost (percentage).

  * For each log:

    * If model is a frontier model (Opus, GPT-4 family) AND total_tokens < 2000 AND description looks like formatting-only / extraction (use simple heuristics: contains "format", "extract", "summarize"), flag as candidate for cheaper model.

Output:
  * “High-cost workflows (>25% total spend):” — list taskTypes exceeding 25% of total cost.

  * “Potential downgrade candidates (frontier model for simple task):” — list sample log lines with suggestion to switch to cheaper model.

  * Suggest caching for patterns where:

    * Same taskType + similar description repeated many times; e.g., group by (taskType, normalized description) and show those with high counts.

Integration pattern:

Provide a wrapper function, e.g. for Python:
   &nbsp;&nbsp;python &nbsp;&nbsp;

  async def call_llm_with_tracking(client, model, messages, task_type, description, source="backend"): # calls client.chat.completions.create(...) # reads usage.prompt_tokens, usage.completion_tokens # computes costEstimate # appends JSONL line # returns the original response

Analogous helper in TypeScript.
Draw from JSON logging and cost-tracking patterns seen in existing open-source cost loggers and JSON logging guides.
If you tell me which language + infra you want (e.g., “Python 3.11, FastAPI, sqlite-vec, Gemini + OpenAI mix”), I can turn any one of these into a fully wired starter repo spec with directory layout, key modules, and a first-pass schema + migration script.
were all of the prompts expanded?
No.
In my last message I only expanded three of your eleven specs into implementation prompts:

Personal CRM Intelligence
Knowledge Base (RAG)
AI Usage and Cost Tracking
Use cases 3, 4, 5, 6, 7, 8, 9, 10 are still in their original form and haven’t been turned into detailed “hand this to a coding agent” prompts yet.​
EXPAND Use cases 3, 4, 5, 6, 7, 8, 9, 10
Here are focused, implementation-ready prompts for Use Cases 3–10 that match the style of the ones you already have. Each is self-contained so you can drop it into a coding assistant.
3) Content Idea Pipeline — Implementation Prompt
You are a senior backend engineer.
Implement a content idea pipeline service that researches topics, dedupes ideas, and creates tasks in a PM tool.
Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite content_ideas.db.
All external API keys from env vars.
Schema:

Table ideas:
  * id TEXT (PK, format YYYY-MM-DD-NNN).

  * date DATE.

  * type TEXT ("short" | "long").

  * title TEXT.

  * slug TEXT UNIQUE (URL-friendly kebab-case).

  * summary TEXT (pitch text).

  * tags TEXT (comma-separated).

  * status TEXT ("pitched" | "accepted" | "rejected" | "produced" | "duplicate").

  * response TEXT (my feedback).

  * embedding BLOB.

  * embedding_dim INT.

  * created_at DATETIME.

Indexes on date, status, and slug.
Step 0 — API surface:

HTTP endpoint POST /ideas/preview:
  * Input: { idea: string, type?: "short" | "long", tags?: string[] }.

  * Returns either a duplicate rejection or a “passes dedupe” brief + recommended task payload.

HTTP endpoint POST /ideas/commit:
  * Input: { idea: string, type?: "short" | "long", tags?: string[], overrideDuplicate?: boolean }.

  * Performs same logic as preview, but if not duplicate (or override true), creates DB row and PM task.

Step 1 — Research:

Given the idea text:
  * If configured, call an X/Twitter search provider (stub compatible with an OpenClaw “x-search” skill).​

  * Query the KB service from Use Case #2 over HTTP with the idea text as query.

  * Optional: simple web search provider wrapper with search(query) → [{title, url, snippet}].

Capture research results in memory:
  * tweets: [{url, text}]

  * kb_sources: [{id, url, title}]

  * web_results: [{title, url}]

Do not persist research by default; it is referenced only in the brief and task body.
Step 2 — Semantic dedupe:

Generate an embedding for the new idea using a pluggable embedding provider (Gemini or OpenAI; same abstraction as RAG KB).
For all existing ideas:
  * Semantic similarity:

    * Load embeddings into memory and compute cosine similarity in.

  * Keyword similarity:

    * Tokenize idea text into normalized terms.

    * For each existing idea, compute:

      * Title overlap score (0–1) × 0.3.

      * Summary overlap score (0–1) × 0.2.

      * Tag overlap score (0–1) × 0.2.

    * Aggregate to keyword score in.

  * Combined:

    * combined = semantic * 0.7 + keyword * 0.3.

Hard gate:
  * If any idea has combined >= 0.4, treat this as duplicate:

    * Return JSON:

      * duplicate: true

      * matched: { id, title, slug, similarity: combined, status }

      * explanation: string.

    * Do not insert into DB unless overrideDuplicate is true on /commit.

Step 3 — Brief assembly (if not duplicate):

Generate a concise brief:
  * 2–4 sentences, no title/hook/script.

  * Explain:

    * The angle.

    * Why it’s worth producing.

    * Ideal format (short vs long).

  * Include bullet list of relevant links:

    * Selected tweets URLs.

    * KB source URLs.

Ensure the brief is plain text / markdown-safe.
Step 4 — Task creation:

Implement TaskManager abstraction with drivers:
  * AsanaTaskManager, LinearTaskManager, NotionTaskManager, TodoistTaskManager.

Choose provider via TASK_MANAGER=asana|linear|notion|todoist env var.
For passing ideas, create a task:
  * title: idea title.

  * description: the brief + related links.

  * labels/tags: mapped from tags.

Return task_url or ID from provider.
Step 5 — Store idea:

Generate ID:
  * YYYY-MM-DD = today.

  * NNN = zero-padded daily sequence (001, 002, …) for that date.

Ensure slug uniqueness (append -2, -3, … on conflict).
Insert into ideas with:
  * status = "pitched".

  * embedding and embedding_dim.

  * tags stored as comma-separated list.

On duplicate override, set status = "pitched" and optionally store the matched original in response.
Step 6 — Notify:

Response from /commit:
  * If duplicate: 409-style JSON with match info.

  * If new:

    * id, slug, title, status, task_url.

Implement tests for the dedupe function with synthetic examples around the 0.4 threshold.
4) Social Media Research System (X/Twitter, Cost-Optimized) — Implementation Prompt
You are a backend engineer.
Implement a cost-optimized X/Twitter research service that answers “What are people saying about [topic]?” with tiered API usage.
Tech & config:

Language: Python 3.11 or Node 20 + TS.
No DB required initially; cache files in local disk (optionally SQLite if cheaper).
Config via env:
  * Tier 2 provider API key (e.g. TwitterAPI.io/SocialData).

  * Tier 3 official X API credentials.

  * Cache directory.

Tiered providers:

Tier 1: FxTwitter API for single-tweet lookups (no auth).​
Tier 2: Paid provider:
  * Must support search, profile lookup, user tweets, and thread expansion.

Tier 3: Official X API v2:
  * Use as fallback with rate limiting (≥350ms between requests).

Query decomposition:

Input: a natural language question.
Use a lightweight LLM or rules to break into 2–4 sub-queries:
  * Different angles: e.g., “use cases”, “criticisms”, “open source alternatives”.

Enforce:
  * Limit total queries to max 4.

  * Deduplicate very similar sub-queries.

Core operations:

Single tweet lookup:
  * Try Tier 1 → if fail/blocked/incomplete, Tier 2 → then Tier 3.

Search:
  * Tier 2 → Tier 3.

Profile lookup:
  * Tier 2 → Tier 3.

Thread expansion:
  * For high-engagement tweets (e.g. top 10 by engagement), expand thread via Tier 2 → Tier 3.

Filtering and ranking:

Filter by timeframe:
  * Default last 7 days; allow overridable param.

Remove:
  * Retweets (keep original).

  * Exact-duplicate texts.

Compute engagement score:
  * engagement = likes + retweets + replies (adjust fields to provider schema).

Basic spam filtering:
  * Drop tweets with:

    * Suspicious repetition (same link + template text).

    * Very low engagement in combination with promotional keywords ("giveaway", "promo", "discount", etc.).

Keep a final set of ~30–100 tweets for synthesis.
Caching:

Cache search results by normalized query + timeframe:
  * Key: hash of (query, timeframe).

  * Store: tweets JSON + timestamp.

  * TTL: 1 hour.

  * If cache hit and not expired, return cached tweets instead of re-calling APIs.

Usage logging:

Maintain one log file per tier:
  * logs/tier1.log, logs/tier2.log, logs/tier3.log.

Each entry:
  * timestamp, tier, operation (search, lookup, thread), params summary, estimated cost.

For Tier 2 and 3:
  * Keep approximate cost per call (configurable via env).

Output synthesis:

For a user query:
  1. Decompose question → 2–4 queries.

  2. Run tiered searches with caching.

  3. Collect and filter tweets.

  4. Compute narratives using LLM:

     * Identify 3–5 key narratives.

     * Identify sentiment (positive/negative/mixed).

     * Extract 5–10 notable tweets with links.

     * Extract 2–3 contrarian or minority takes.

Output JSON:
   &nbsp;&nbsp;json &nbsp;&nbsp;

  { "narratives": [{ "title": "...", "description": "..." }], "sentiment": "positive|negative|mixed", "notable_tweets": [{ "url": "...", "text": "...", "engagement": { "likes": 0, "retweets": 0, "replies": 0 } }], "contrarian_takes": [{ "url": "...", "text": "..." }] }

Provide:

POST /social/x/research endpoint:
  * Body: { "question": string, "timeframe_days"?: number }.

Configurable logs directory and cache directory.  Use the OpenClaw “x-search” skill pattern and cost-optimized search strategies as inspiration, but implement a standalone service.
YouTube Analytics + Competitor Tracking — Implementation Prompt
You are a data engineer.Implement a YouTube analytics + competitor tracking service with daily metrics and chart generation.
Tech & storage:
Language: Python 3.11.
Use YouTube Data API + YouTube Analytics API.
Database: SQLite youtube_analytics.db.
Charts: matplotlib (dark theme), PNG output.
Schema:

videos:
  * id TEXT (YouTube video ID, PK).

  * title TEXT.

  * published_at DATETIME.

  * thumbnail_url TEXT.

  * duration_seconds INTEGER.

daily_stats:
  * video_id TEXT (FK → videos.id).

  * date DATE.

  * views INTEGER.

  * watch_time_minutes REAL.

  * avg_view_duration REAL (seconds or ISO string, pick one and document).

  * impressions INTEGER NULLABLE.

  * ctr REAL NULLABLE.

  * subscribers_gained INTEGER.

  * PK: (video_id, date).

competitors:
  * channel_id TEXT (PK).

  * name TEXT.

competitor_stats:
  * channel_id TEXT (FK → competitors.channel_id).

  * date DATE.

  * subscribers INTEGER.

  * video_count INTEGER.

competitor_uploads:
  * channel_id TEXT.

  * video_id TEXT.

  * title TEXT.

  * published_at DATETIME.

  * views INTEGER.

  * PK: (channel_id, video_id).

Data collection (daily cron):

For my channel:
  * Input: MY_CHANNEL_ID env var.

  * Fetch channel’s videos and maintain videos table (incremental).

  * For each day:

    * Use Analytics API to fetch:

      * Views, estimated minutes watched, average view duration, impressions, CTR, subscribers gained.

    * Upsert into daily_stats.

For competitors:
  * Config: COMPETITOR_CHANNEL_IDS comma-separated env var.

  * For each competitor daily:

    * Fetch subscriber count and video count → competitor_stats.

    * Fetch recent uploads (e.g., last 30 days or last N videos) with title, publish date, views → competitor_uploads.

Derived metrics:

7-day moving average for daily views per video and channel-wide:
  * Precompute or compute in queries.

Subscriber-to-view conversion rate:
  * subscribers_gained / total_views on a given day or period.

Views-per-video trend:
  * Time-series of per-video average views.

Chart generation (matplotlib, dark theme):

Use a dedicated module charts.py with:
  * generate_trend_chart(video_id, output_path):

    * Line chart of daily views for the video, fill under the line.

  * generate_top_videos_chart(start_date, end_date, top_n, output_path):

    * Horizontal bar chart of top N videos by views in the period.

    * Color-code bars by short vs long-form (e.g., duration <60s).

  * generate_daily_channel_chart(start_date, end_date, output_path):

    * Daily total channel views with 7-day moving average overlay.

Formatting:
  * Dark background, readable grid and axis labels.

  * Numbers formatted with commas, readable date labels.

  * Save to designated charts/ directory.

API & CLI:

CLI commands:
  * collect-my-channel (daily).

  * collect-competitors (daily).

  * charts trend <video_id>.

  * charts top --start YYYY-MM-DD --end YYYY-MM-DD --top 10.

Optionally, HTTP endpoints:
  * GET /videos/top?start=...&end=....

  * GET /charts/trend/:video_id (serves PNG).

Schedule via cron externally (e.g., 0 3 * * * python -m yt_analytics collect-my-channel).
Guard for rate limits, use pagination, and log all API errors with enough detail for debugging.
6) Nightly Business Briefing (Multi-Perspective AI Council) — Implementation Prompt
You are building an AI council pipeline that turns normalized signals into nightly recommendations.
Tech:

Language: Python 3.11.
LLM provider: configurable (Anthropic, OpenAI, etc.).
Storage: SQLite business_council.db.
Signal ingestion:

Define a JSON structure for signals:
   &nbsp;&nbsp;json &nbsp;&nbsp;

  { "source": "string", "signal_name": "string", "value": "number|string", "confidence": 0-100, "direction": "up|down|flat", "category": "string", "sample_events": [ ... ] }

Implement adapters for sources:
  * YouTube (from Use Case #5).

  * CRM health (from Use Case #1).

  * Project management backlog.

  * Social metrics, sales pipeline, operations/cron reliability.

Normalize all adapters to this structure and dump into a list nightly.
Trim to top 200 signals by confidence with up to 4 sample events per source.
DB schema:

runs:
  * id INTEGER PK.

  * run_date DATE.

  * status TEXT.

recommendations:
  * id INTEGER PK.

  * run_id INTEGER FK.

  * title TEXT.

  * description TEXT.

  * evidence TEXT (JSON array of signal refs).

  * impact INTEGER.

  * effort INTEGER.

  * confidence INTEGER.

  * priority REAL.

  * filtered_out BOOLEAN.

policy:
  * id INTEGER PK (single row).

  * w1 REAL (impact weight).

  * w2 REAL (confidence weight).

  * w3 REAL (effort weight).

Phase 1 — Draft (LeadAnalyst persona):

Call a high-end model with:
  * System prompt describing LeadAnalyst (weekly/monthly outlook scoring, constraints).

  * Input: signals (compact summary) and past 3 runs’ recommendations (optional).

Output (JSON):
  * weekly_outlook 0–100.

  * monthly_outlook 0–100.

  * blended_outlook 0–100.

  * recommendations: list of:

    * title, description, evidence_refs, impact, effort, confidence.

Validate and coerce fields into expected ranges.
Phase 2 — Parallel review (4 personas):

Personas:
  * GrowthStrategist.

  * RevenueGuardian.

  * SkepticalOperator.

  * TeamDynamicsArchitect.

For each persona, call LLM in parallel with:
  * Draft recommendations.

  * Signals summary.

  * Persona-specific instructions (what to care about, when to reject/change).

Expected JSON per reviewer:
  * reviewer_name.

  * key_findings: list of strings.

  * votes: for each draft recommendation:

    * title, stance (support|revise|reject), adjustments to impact|effort|confidence (optional).

  * new_recommendations: zero or more additional recs in same structure as draft.

Phase 3 — Consensus (CouncilModerator persona):

Inputs:
  * Draft recommendations.

  * All 4 reviews.

LLM output:
  * Final normalized recommendation list:

    * For each: title, description, evidence refs, impact, effort, confidence, consensus_notes.

Fallback:
  * If consensus call fails, fall back to draft recs.

  * If any reviewer fails, treat as stub with error message.

Priority scoring and filtering:

From policy table, load:
  * w1 (default 0.4), w2 (0.35), w3 (0.25).

Compute:
  * priority = impact * w1 + confidence * w2 + (100 - effort) * w3.

Hard filter:
  * Remove recommendations that explicitly say “publish now” or equivalent.

  * Detect via simple keyword scan in title/description.

Store final recs in recommendations table.
Output:

Nightly run writes:
  * JSON summary file.

  * DB entries.

Provide CLI:
  * run-council to execute full pipeline.

  * show-latest to print ranked recommendations with priority and consensus notes.

Optionally send summary to Slack/Matrix/Telegram via webhook.
Use OpenClaw multi-agent council patterns as conceptual inspiration, but implement as explicit LLM calls with strict JSON schemas.
7) CRM/Business Tool Natural Language Access — Implementation Prompt
You are implementing a natural-language interface to a CRM (HubSpot/Salesforce/other).
Tech:

Language: Node 20 + TypeScript or Python 3.11.
Integrate with one CRM first (HubSpot recommended), but keep the interface pluggable.
Authentication: API key or OAuth from env.
Supported objects & operations:

Contacts: search, create, update, list, get by ID.
Companies: search, create, update, list.
Deals: search, create, update, list, query by pipeline stage.
Owners: list.
Associations:
  * Contacts ↔ Companies.

  * Deals ↔ Contacts.

  * Deals ↔ Companies.

  * For HubSpot, support association type IDs 1, 3, 5 (configurable).

NL interface:

Expose HTTP POST /crm/nl:
  * Input: { "query": string }.

Pipeline:
  1. Intent classification:

     * Use either rules or an LLM (cheap model) to classify intent:

       * lookup, create, update, list, associate.

  2. Entity extraction:

     * Extract object type (contact/company/deal), key fields (name, email, etc.), filters (stage, owner).

  3. Validation:

     * If required fields missing, respond with clarifying questions instead of hitting CRM.

  4. Execution:

     * Map to CRM API calls via a CrmClient interface.

  5. Presentation:

     * Return clean human-readable summaries, not raw JSON.

Examples:

“Find the contact for Jane Doe at Acme” → search contacts with name and company.
“Add a new contact: John Smith, john@foo.com, at FooCorp” → create contact.
“Update Jane Doe’s title to VP Marketing” → search then update.
“Show all deals in negotiation stage” → list deals filtered by stage.
“Link Jane Doe to Acme Corp” → contact-company association.
Response formatting:

Contacts:
  * Name, email, phone, company, role, last activity date (if available).

Deals:
  * Name, amount, stage, close date, last modified.

Companies:
  * Name, domain, owner, lifecycle stage.

Implement:

CrmClient with:
  * searchContacts, createContact, updateContact, listContacts, getContactById.

  * Similar for companies and deals.

  * associateContactCompany, associateDealContact, associateDealCompany.

Intent & extraction using:
  * Lightweight LLM call with JSON output (intent + slots), or a robust regex/rule layer plus LLM fallback.

Add:

GET /crm/schema endpoint:
  * Lists available fields/properties for each object (via CRM API call).

Error handling and rate limit handling.
Use best practices from typical NL-to-CRM assistants and OpenClaw CRM skills, but implement all adapter code directly against the official CRM REST API.
8) AI Content Humanization — Implementation Prompt
You are implementing a text humanizer that rewrites AI-ish text to sound more natural.
Tech:

Language: Python or Node.
Use a single LLM (cheap model) with strong instruction prompt.
Provide a simple HTTP API and CLI.
API:

POST /humanize:
  * Input: { "text": string, "channel"?: "twitter" | "linkedin" | "blog" | "email", "explainChanges"?: boolean }.

Output:
  * { "rewritten": string, "notes"?: [{ "before": string, "after": string, "reason": string }] }.

Step 1 — Detection:

Implement a detection function that:
  * Scans for overused phrases:

    * “delve”, “landscape”, “leverage”, “it’s important to note”, “in conclusion”, “game-changing”, “revolutionary”, “transformative”.

  * Looks for:

    * Repetitive sentence beginnings.

    * Excessive hedging (“worth noting that perhaps”, etc.).

    * Too-regular paragraph length or bullet patterns.

  * Provide this detection summary to the LLM as context (counts + examples).

Step 2 — Rewrite:

LLM system instructions:
  * Replace vague qualifiers with specific, concrete language.

  * Vary sentence length and rhythm.

  * Use contractions and natural phrasing where appropriate.

  * Remove filler, keep core message.

  * Keep semantics intact, do not invent facts.

Prompt structure:
  * Provide original text.

  * Provide detection summary.

  * Provide channel and channel guidelines:

    * Twitter: ≤280 chars, punchy, direct.

    * LinkedIn: professional, conversational, no fluff.

    * Blog: relaxed, can include light personal phrasing, but no fake anecdotes.

    * Email: clear, action-oriented, friendly but concise.

  * Request JSON output with rewritten and optionally notes if explainChanges is true.

Guardrails:

Never change numbers, dates, or explicit commitments.
Preserve URLs, code blocks, markdown, and hashtags.
If channel is Twitter, ensure length ≤280 chars (count plain text).
CLI:

humanize <file> → prints rewritten text.
humanize --channel=linkedin to tune tone.
Use OpenClaw’s content editing/humanization skills as conceptual reference, but build a focused humanizer with clear IO and detection.
9) Image Generation + Iterative Editing — Implementation Prompt
You are implementing an image generation + iterative editing workflow around an image generation API.
Tech:

Language: Node 20 + TS or Python.
Image provider: pluggable (DALL·E, Stable Diffusion API, Flux, etc.).
Storage: local images/ directory; optional metadata DB (SQLite).
Core flow:

Maintain a sessions table (or JSON file) to track context:
  * id, created_at, state (JSON with current prompt, style, constraints, history).

API:
  * POST /image/start:

    * Input: { "description": string } (subject, style, mood, colors, any text).

    * Creates a new session, generates 1–3 initial variants.

  * POST /image/iterate:

    * Input: { "session_id": string, "feedback": string }.

    * Uses previous state + feedback to refine and generate new variants.

  * POST /image/edit:

    * Input: { "session_id"?: string, "image_id": string, "instructions": string }.

    * Applies editing (background change, composition, etc.) via img2img/inpainting.

Generation details:

For each generation or edit:
  * Save resulting images to disk (images/<session>/<variant>.png).

  * Store metadata:

    * Prompt used, seed (if exposed), provider/model, parent image (for edits).

Track:
  * Final selection:

    * POST /image/accept:

      * Input: { "session_id": string, "image_id": string }.

      * Marks that variant as final in metadata.

Context tracking:

Session state stores:
  * Original user description.

  * Last accepted or current best prompt.

  * A running list of user feedback messages.

When generating new variants:
  * Construct a new prompt by combining:

    * Original description.

    * Past constraints.

    * Most recent feedback instructions.

Output:

For each API call returning images:
  * Return:

    * session_id

    * array of { image_id, preview_url_or_path }.

Keep provider interface abstract (ImageProvider.generate, ImageProvider.edit) so you can swap DALL·E vs SDXL etc. Later you can plug this into OpenClaw as a skill.
10) Task Management from Meetings + Chat — Implementation Prompt
You are building a task extraction and approval system from meeting transcripts and chat.
Tech:

Language: Python 3.11 or Node 20 + TS.
External:
  * Transcript input (file upload or webhook).

  * Task manager (Todoist / Asana / Linear / Notion), via adapter.

Schema:

SQLite tasks_from_meetings.db:
  * extracted_items:

    * id INTEGER PK.

    * source_type TEXT ("meeting" | "note" | "direct").

    * source_id TEXT (e.g., meeting ID or note ID).

    * description TEXT (max 150 chars).

    * assignee TEXT.

    * is_owner BOOLEAN.

    * todoist_title TEXT (for owner tasks).

    * status TEXT ("pending" | "approved" | "rejected" | "created" | "failed").

    * task_external_id TEXT NULLABLE.

    * created_at DATETIME.

    * updated_at DATETIME.

Input sources:

Meeting transcripts:
  * Upload endpoint POST /meetings/:id/transcript (raw text or file).

Notes:
  * POST /notes with { "text": string }.

Direct commands:
  * POST /tasks/direct with { "text": string } (e.g., “remind me to X by Friday”).

Extraction:

Use a fast LLM with explicit JSON schema:
  * For transcripts and notes:

    * Ask LLM to produce an array of:

       &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;json &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

      { "description": "max 150 chars, concrete", "assignee": "string", "is_owner": true|false, "todoist_title": "max 120 chars or null" }

  * Provide rules:

    * No vague items (“think about”, “discuss later”).

    * Only actionable tasks with clear next step.

    * todoist_title only when is_owner is true.

Store extracted items with status = "pending".
CRM cross-reference (optional):

If Use Case #1 CRM exists, call it to enrich assignee with company/role, but keep this as optional metadata only.
Approval flow:

GET /tasks/pending → list pending items grouped by source_id.
UX flow (handled by client or CLI, but backend must support):
  1. Show numbered list of tasks.

  2. Accept input indicating:

     * “all”, “none”, or selected IDs (e.g., [1,3,5]).

  3. Editing:

     * PATCH /tasks/:id to modify title/description/due date before creation.

  4. Confirm:

     * POST /tasks/approve with selected ids.

Task creation:

Once approved:
  * Call task manager adapter:

    * For Todoist: use REST API to create tasks with title, description, due date, project.

  * Update status:

    * created if API success + store task_external_id.

    * failed if API errors; leave for retry.

Retry mechanism:
  * Simple CLI or endpoint POST /tasks/retry-failed.

Direct task creation:

For tasks/direct:
  * Use a small LLM or parser to extract:

    * Task description.

    * Due date/time (natural language parsing).

  * Confirm with user (or parameter autoCreate: boolean).

  * Create directly in the task manager (skip extraction pipeline but still log to DB with source_type = "direct").

Output:

When tasks are created, respond with:
  * tasks: [{ id, description, status, external_link }].

Take inspiration from OpenClaw meeting-followup automations, but implement a conservative, approval-gated pipeline that never auto-creates tasks without confirmation.
If you tell me your preferred base stack (e.g., “Python, FastAPI, SQLite-vec, Gemini + OpenAI”), I can collapse any one of these into a repo layout (folders, key modules, first migrations) tailored to OpenClaw skill integration.
Follow-ups
Implementation prompt for OpenClaw meeting transcription use case
Step-by-step guide to set up OpenClaw content idea pipeline
How to install and configure X/Twitter search skill in OpenClaw
Tutorial for OpenClaw OpusClip video content automation
Example cron jobs for daily OpenClaw content generation

3) Content Idea Pipeline — Implementation Prompt
You are a senior backend engineer.
Implement a content idea pipeline service that researches topics, dedupes ideas, and creates tasks in a PM tool.

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite content_ideas.db.
All external API keys from env vars.
Schema:

Table ideas:
id TEXT (PK, format YYYY-MM-DD-NNN).
date DATE.
type TEXT ("short" | "long").
title TEXT.
slug TEXT UNIQUE (URL-friendly kebab-case).
summary TEXT (pitch text).
tags TEXT (comma-separated).
status TEXT ("pitched" | "accepted" | "rejected" | "produced" | "duplicate").
response TEXT (my feedback).
embedding BLOB.
embedding_dim INT.
created_at DATETIME.
Indexes on date, status, and slug.
Step 0 — API surface:

HTTP endpoint POST /ideas/preview:
Input: { idea: string, type?: "short" | "long", tags?: string[] }.
Returns either a duplicate rejection or a “passes dedupe” brief + recommended task payload.
HTTP endpoint POST /ideas/commit:
Input: { idea: string, type?: "short" | "long", tags?: string[], overrideDuplicate?: boolean }.
Performs same logic as preview, but if not duplicate (or override true), creates DB row and PM task.
Step 1 — Research:

Given the idea text:
If configured, call an X/Twitter search provider (stub compatible with an OpenClaw “x-search” skill).
Query the KB service from Use Case #2 over HTTP with the idea text as query.
Optional: simple web search provider wrapper with search(query) → [{title, url, snippet}].
Capture research results in memory:
tweets: [{url, text}]
kb_sources: [{id, url, title}]
web_results: [{title, url}]
Do not persist research by default; it is referenced only in the brief and task body.
Step 2 — Semantic dedupe:

Generate an embedding for the new idea using a pluggable embedding provider (Gemini or OpenAI; same abstraction as RAG KB).
For all existing ideas:
Semantic similarity:
Load embeddings into memory and compute cosine similarity.
Keyword similarity:
Tokenize idea text into normalized terms.
For each existing idea, compute:
Title overlap score (0–1) × 0.3.
Summary overlap score (0–1) × 0.2.
Tag overlap score (0–1) × 0.2.
Aggregate to keyword score.
Combined:
combined = semantic * 0.7 + keyword * 0.3.
Hard gate:
If any idea has combined >= 0.4, treat this as duplicate:
Return JSON:
duplicate: true
matched: { id, title, slug, similarity: combined, status }
explanation: string.
Do not insert into DB unless overrideDuplicate is true on /commit.
Step 3 — Brief assembly (if not duplicate):

Generate a concise brief:
2–4 sentences, no title/hook/script.
Explain:
The angle.
Why it’s worth producing.
Ideal format (short vs long).
Include bullet list of relevant links:
Selected tweets URLs.
KB source URLs.
Ensure the brief is plain text / markdown-safe.
Step 4 — Task creation:

Implement TaskManager abstraction with drivers:
AsanaTaskManager, LinearTaskManager, NotionTaskManager, TodoistTaskManager.
Choose provider via TASK_MANAGER=asana|linear|notion|todoist env var.
For passing ideas, create a task:
title: idea title.
description: the brief + related links.
labels/tags: mapped from tags.
Return task_url or ID from provider.
Step 5 — Store idea:

Generate ID:
YYYY-MM-DD = today.
NNN = zero-padded daily sequence (001, 002, …) for that date.
Ensure slug uniqueness (append -2, -3, … on conflict).
Insert into ideas with:
status = "pitched".
embedding and embedding_dim.
tags stored as comma-separated list.
On duplicate override, set status = "pitched" and optionally store the matched original in response.
Step 6 — Notify:

Response from /commit:
If duplicate: 409-style JSON with match info.
If new:
id, slug, title, status, task_url.
Implement tests for the dedupe function with synthetic examples around the 0.4 threshold.

4) Social Media Research System (Cost-Optimized) — Implementation Prompt
You are a backend engineer.
Implement a cost-optimized X/Twitter research service that answers “What are people saying about [topic]?” with tiered API usage.

Tech & config:

Language: Python 3.11 or Node 20 + TS.
No DB required initially; cache files in local disk (optionally SQLite if cheaper).
Config via env:
Tier 2 provider API key (e.g. TwitterAPI.io/SocialData).
Tier 3 official X API credentials.
Cache directory.
Tiered providers:

Tier 1: FxTwitter API for single-tweet lookups (no auth).
Tier 2: Paid provider:
Must support search, profile lookup, user tweets, and thread expansion.
Tier 3: Official X API v2:
Use as fallback with rate limiting (≥350ms between requests).
Query decomposition:

Input: a natural language question.
Use a lightweight LLM or rules to break into 2–4 sub-queries:
Different angles: e.g., “use cases”, “criticisms”, “open source alternatives”.
Enforce:
Limit total queries to max 4.
Deduplicate very similar sub-queries.
Core operations:

Single tweet lookup:
Try Tier 1 → if fail/blocked/incomplete, Tier 2 → then Tier 3.
Search:
Tier 2 → Tier 3.
Profile lookup:
Tier 2 → Tier 3.
Thread expansion:
For high-engagement tweets (e.g. top 10 by engagement), expand thread via Tier 2 → Tier 3.
Filtering and ranking:

Filter by timeframe:
Default last 7 days; allow overridable param.
Remove:
Retweets (keep original).
Exact-duplicate texts.
Compute engagement score:
engagement = likes + retweets + replies (adjust fields to provider schema).
Basic spam filtering:
Drop tweets with:
Suspicious repetition (same link + template text).
Very low engagement in combination with promotional keywords ("giveaway", "promo", "discount", etc.).
Keep a final set of ~30–100 tweets for synthesis.
Caching:

Cache search results by normalized query + timeframe:
Key: hash of (query, timeframe).
Store: tweets JSON + timestamp.
TTL: 1 hour.
If cache hit and not expired, return cached tweets instead of re-calling APIs.
Usage logging:

Maintain one log file per tier:
logs/tier1.log, logs/tier2.log, logs/tier3.log.
Each entry:
timestamp, tier, operation (search, lookup, thread), params summary, estimated cost.
For Tier 2 and 3:
Keep approximate cost per call (configurable via env).
Output synthesis:

For a user query:
Decompose question → 2–4 queries.
Run tiered searches with caching.
Collect and filter tweets.
Compute narratives using LLM:
Identify 3–5 key narratives.
Identify sentiment (positive/negative/mixed).
Extract 5–10 notable tweets with links.
Extract 2–3 contrarian or minority takes.
Output JSON:
{ "narratives": [{ "title": "...", "description": "..." }], "sentiment": "positive|negative|mixed", "notable_tweets": [{ "url": "...", "text": "...", "engagement": { "likes": 0, "retweets": 0, "replies": 0 } }], "contrarian_takes": [{ "url": "...", "text": "..." }] }
Provide:

POST /social/x/research endpoint:
Body: { "question": string, "timeframe_days"?: number }.
Configurable logs directory and cache directory.
Use the OpenClaw “x-search” skill pattern and cost-optimized search strategies as inspiration, but implement a standalone service.
5) YouTube Analytics + Competitor Tracking — Implementation Prompt
You are a data engineer.
Implement a YouTube analytics + competitor tracking service with daily metrics and chart generation.

Tech & storage:

Language: Python 3.11.
Use YouTube Data API + YouTube Analytics API.
Database: SQLite youtube_analytics.db.
Charts: matplotlib (dark theme), PNG output.
Schema:

videos:
id TEXT (YouTube video ID, PK).
title TEXT.
published_at DATETIME.
thumbnail_url TEXT.
duration_seconds INTEGER.
daily_stats:
video_id TEXT (FK → videos.id).
date DATE.
views INTEGER.
watch_time_minutes REAL.
avg_view_duration REAL (seconds or ISO string, pick one and document).
impressions INTEGER NULLABLE.
ctr REAL NULLABLE.
subscribers_gained INTEGER.
PK: (video_id, date).
competitors:
channel_id TEXT (PK).
name TEXT.
competitor_stats:
channel_id TEXT (FK → competitors.channel_id).
date DATE.
subscribers INTEGER.
video_count INTEGER.
competitor_uploads:
channel_id TEXT.
video_id TEXT.
title TEXT.
published_at DATETIME.
views INTEGER.
PK: (channel_id, video_id).
Data collection (daily cron):

For my channel:
Input: MY_CHANNEL_ID env var.
Fetch channel’s videos and maintain videos table (incremental).
For each day:
Use Analytics API to fetch:
Views, estimated minutes watched, average view duration, impressions, CTR, subscribers gained.
Upsert into daily_stats.
For competitors:
Config: COMPETITOR_CHANNEL_IDS comma-separated env var.
For each competitor daily:
Fetch subscriber count and video count → competitor_stats.
Fetch recent uploads (e.g., last 30 days or last N videos) with title, publish date, views → competitor_uploads.
Derived metrics:

7-day moving average for daily views per video and channel-wide:
Precompute or compute in queries.
Subscriber-to-view conversion rate:
subscribers_gained / total_views on a given day or period.
Views-per-video trend:
Time-series of per-video average views.
Chart generation (matplotlib, dark theme):

Use a dedicated module charts.py with:
generate_trend_chart(video_id, output_path):
Line chart of daily views for the video, fill under the line.
generate_top_videos_chart(start_date, end_date, top_n, output_path):
Horizontal bar chart of top N videos by views in the period.
Color-code bars by short vs long-form (e.g., duration <60s).
generate_daily_channel_chart(start_date, end_date, output_path):
Daily total channel views with 7-day moving average overlay.
Formatting:
Dark background, readable grid and axis labels.
Numbers formatted with commas, readable date labels.
Save to designated charts/ directory.
API & CLI:

CLI commands:
collect-my-channel (daily).
collect-competitors (daily).
charts trend <video_id>.
charts top --start YYYY-MM-DD --end YYYY-MM-DD --top 10.
Optionally, HTTP endpoints:
GET /videos/top?start=...&end=....
GET /charts/trend/:video_id (serves PNG).
Schedule via cron externally (e.g., 0 3 * * * python -m yt_analytics collect-my-channel).
Guard for rate limits, use pagination, and log all API errors with enough detail for debugging.

6) Nightly Business Briefing (Multi-Perspective AI Council) — Implementation Prompt
You are building an AI council pipeline that turns normalized signals into nightly recommendations.

Tech:

Language: Python 3.11.
LLM provider: configurable (Anthropic, OpenAI, etc.).
Storage: SQLite business_council.db.
Signal ingestion:

Define a JSON structure for signals:
{ "source": "string", "signal_name": "string", "value": "number|string", "confidence": 0-100, "direction": "up|down|flat", "category": "string", "sample_events": [ ... ] }
Implement adapters for sources:
YouTube (from Use Case #5).
CRM health (from Use Case #1).
Project management backlog.
Social metrics, sales pipeline, operations/cron reliability.
Normalize all adapters to this structure and dump into a list nightly.
Trim to top 200 signals by confidence with up to 4 sample events per source.
DB schema:

runs:
id INTEGER PK.
run_date DATE.
status TEXT.
recommendations:
id INTEGER PK.
run_id INTEGER FK.
title TEXT.
description TEXT.
evidence TEXT (JSON array of signal refs).
impact INTEGER.
effort INTEGER.
confidence INTEGER.
priority REAL.
filtered_out BOOLEAN.
policy:
id INTEGER PK (single row).
w1 REAL (impact weight).
w2 REAL (confidence weight).
w3 REAL (effort weight).
Phase 1 — Draft (LeadAnalyst persona):

Call a high-end model with:
System prompt describing LeadAnalyst (weekly/monthly outlook scoring, constraints).
Input: signals (compact summary) and past 3 runs’ recommendations (optional).
Output (JSON):
weekly_outlook 0–100.
monthly_outlook 0–100.
blended_outlook 0–100.
recommendations: list of:
title, description, evidence_refs, impact, effort, confidence.
Validate and coerce fields into expected ranges.
Phase 2 — Parallel review (4 personas):

Personas:
GrowthStrategist.
RevenueGuardian.
SkepticalOperator.
TeamDynamicsArchitect.
For each persona, call LLM in parallel with:
Draft recommendations.
Signals summary.
Persona-specific instructions (what to care about, when to reject/change).
Expected JSON per reviewer:
reviewer_name.
key_findings: list of strings.
votes: for each draft recommendation:
title, stance (support|revise|reject), adjustments to impact|effort|confidence (optional).
new_recommendations: zero or more additional recs in same structure as draft.
Phase 3 — Consensus (CouncilModerator persona):

Inputs:
Draft recommendations.
All 4 reviews.
LLM output:
Final normalized recommendation list:
For each: title, description, evidence refs, impact, effort, confidence, consensus_notes.
Fallback:
If consensus call fails, fall back to draft recs.
If any reviewer fails, treat as stub with error message.
Priority scoring and filtering:

From policy table, load:
w1 (default 0.4), w2 (0.35), w3 (0.25).
Compute:
priority = impact * w1 + confidence * w2 + (100 - effort) * w3.
Hard filter:
Remove recommendations that explicitly say “publish now” or equivalent.
Detect via simple keyword scan in title/description.
Store final recs in recommendations table.
Output:

Nightly run writes:
JSON summary file.
DB entries.
Provide CLI:
run-council to execute full pipeline.
show-latest to print ranked recommendations with priority and consensus notes.
Optionally send summary to Slack/Matrix/Telegram via webhook.
Use OpenClaw multi-agent council patterns as conceptual inspiration, but implement as explicit LLM calls with strict JSON schemas.

7) CRM/Business Tool Natural Language Access — Implementation Prompt
You are implementing a natural-language interface to a CRM (HubSpot/Salesforce/other).

Tech:

Language: Node 20 + TypeScript or Python 3.11.
Integrate with one CRM first (HubSpot recommended), but keep the interface pluggable.
Authentication: API key or OAuth from env.
Supported objects & operations:

Contacts: search, create, update, list, get by ID.
Companies: search, create, update, list.
Deals: search, create, update, list, query by pipeline stage.
Owners: list.
Associations:
Contacts ↔ Companies.
Deals ↔ Contacts.
Deals ↔ Companies.
For HubSpot, support association type IDs 1, 3, 5 (configurable).
NL interface:

Expose HTTP POST /crm/nl:
Input: { "query": string }.
Pipeline:
Intent classification:
Use either rules or an LLM (cheap model) to classify intent:
lookup, create, update, list, associate.
Entity extraction:
Extract object type (contact/company/deal), key fields (name, email, etc.), filters (stage, owner).
Validation:
If required fields missing, respond with clarifying questions instead of hitting CRM.
Execution:
Map to CRM API calls via a CrmClient interface.
Presentation:
Return clean human-readable summaries, not raw JSON.
Examples:

“Find the contact for Jane Doe at Acme” → search contacts with name and company.
“Add a new contact: John Smith, john@foo.com, at FooCorp” → create contact.
“Update Jane Doe’s title to VP Marketing” → search then update.
“Show all deals in negotiation stage” → list deals filtered by stage.
“Link Jane Doe to Acme Corp” → contact-company association.
Response formatting:

Contacts:
Name, email, phone, company, role, last activity date (if available).
Deals:
Name, amount, stage, close date, last modified.
Companies:
Name, domain, owner, lifecycle stage.
Implement:

CrmClient with:
searchContacts, createContact, updateContact, listContacts, getContactById.
Similar for companies and deals.
associateContactCompany, associateDealContact, associateDealCompany.
Intent & extraction using:
Lightweight LLM call with JSON output (intent + slots), or a robust regex/rule layer plus LLM fallback.
Add:

GET /crm/schema endpoint:
Lists available fields/properties for each object (via CRM API call).
Error handling and rate limit handling.
Use best practices from typical NL-to-CRM assistants and OpenClaw CRM skills, but implement all adapter code directly against the official CRM REST API.

8) AI Content Humanization — Implementation Prompt
You are implementing a text humanizer that rewrites AI-ish text to sound more natural.

Tech:

Language: Python or Node.
Use a single LLM (cheap model) with strong instruction prompt.
Provide a simple HTTP API and CLI.
API:

POST /humanize:
Input: { "text": string, "channel"?: "twitter" | "linkedin" | "blog" | "email", "explainChanges"?: boolean }.
Output:
{ "rewritten": string, "notes"?: [{ "before": string, "after": string, "reason": string }] }.
Step 1 — Detection:

Implement a detection function that:
Scans for overused phrases:
“delve”, “landscape”, “leverage”, “it’s important to note”, “in conclusion”, “game-changing”, “revolutionary”, “transformative”.
Looks for:
Repetitive sentence beginnings.
Excessive hedging (“worth noting that perhaps”, etc.).
Too-regular paragraph length or bullet patterns.
Provide this detection summary to the LLM as context (counts + examples).
Step 2 — Rewrite:

LLM system instructions:
Replace vague qualifiers with specific, concrete language.
Vary sentence length and rhythm.
Use contractions and natural phrasing where appropriate.
Remove filler, keep core message.
Keep semantics intact, do not invent facts.
Prompt structure:
Provide original text.
Provide detection summary.
Provide channel and channel guidelines:
Twitter: ≤280 chars, punchy, direct.
LinkedIn: professional, conversational, no fluff.
Blog: relaxed, can include light personal phrasing, but no fake anecdotes.
Email: clear, action-oriented, friendly but concise.
Request JSON output with rewritten and optionally notes if explainChanges is true.
Guardrails:

Never change numbers, dates, or explicit commitments.
Preserve URLs, code blocks, markdown, and hashtags.
If channel is Twitter, ensure length ≤280 chars (count plain text).
CLI:

humanize <file> → prints rewritten text.
humanize --channel=linkedin to tune tone.
Use OpenClaw’s content editing/humanization skills as conceptual reference, but build a focused humanizer with clear IO and detection.

9) Image Generation + Iterative Editing — Implementation Prompt
You are implementing an image generation + iterative editing workflow around an image generation API.

Tech:

Language: Node 20 + TS or Python.
Image provider: pluggable (DALL·E, Stable Diffusion API, Flux, etc.).
Storage: local images/ directory; optional metadata DB (SQLite).
Core flow:

Maintain a sessions table (or JSON file) to track context:
id, created_at, state (JSON with current prompt, style, constraints, history).
API:
POST /image/start:
Input: { "description": string } (subject, style, mood, colors, any text).
Creates a new session, generates 1–3 initial variants.
POST /image/iterate:
Input: { "session_id": string, "feedback": string }.
Uses previous state + feedback to refine and generate new variants.
POST /image/edit:
Input: { "session_id"?: string, "image_id": string, "instructions": string }.
Applies editing (background change, composition, etc.) via img2img/inpainting.
Generation details:

For each generation or edit:
Save resulting images to disk (images/<session>/<variant>.png).
Store metadata:
Prompt used, seed (if exposed), provider/model, parent image (for edits).
Track:
Final selection:
POST /image/accept:
Input: { "session_id": string, "image_id": string }.
Marks that variant as final in metadata.
Context tracking:

Session state stores:
Original user description.
Last accepted or current best prompt.
A running list of user feedback messages.
When generating new variants:
Construct a new prompt by combining:
Original description.
Past constraints.
Most recent feedback instructions.
Output:

For each API call returning images:
Return:
session_id
array of { image_id, preview_url_or_path }.
Keep provider interface abstract (ImageProvider.generate, ImageProvider.edit) so you can swap DALL·E vs SDXL etc. Later you can plug this into OpenClaw as a skill.

10) Task Management from Meetings + Chat — Implementation Prompt
You are building a task extraction and approval system from meeting transcripts and chat.

Tech:

Language: Python 3.11 or Node 20 + TS.
External:
Transcript input (file upload or webhook).
Task manager (Todoist / Asana / Linear / Notion), via adapter.
Schema:

SQLite tasks_from_meetings.db:
extracted_items:
id INTEGER PK.
source_type TEXT ("meeting" | "note" | "direct").
source_id TEXT (e.g., meeting ID or note ID).
description TEXT (max 150 chars).
assignee TEXT.
is_owner BOOLEAN.
todoist_title TEXT (for owner tasks).
status TEXT ("pending" | "approved" | "rejected" | "created" | "failed").
task_external_id TEXT NULLABLE.
created_at DATETIME.
updated_at DATETIME.
Input sources:

Meeting transcripts:
Upload endpoint POST /meetings/:id/transcript (raw text or file).
Notes:
POST /notes with { "text": string }.
Direct commands:
POST /tasks/direct with { "text": string } (e.g., “remind me to X by Friday”).
Extraction:

Use a fast LLM with explicit JSON schema:
For transcripts and notes:
Ask LLM to produce an array of:
{ "description": "max 150 chars, concrete", "assignee": "string", "is_owner": true|false, "todoist_title": "max 120 chars or null" }
Provide rules:
No vague items (“think about”, “discuss later”).
Only actionable tasks with clear next step.
todoist_title only when is_owner is true.
Store extracted items with status = "pending".
CRM cross-reference (optional):

If Use Case #1 CRM exists, call it to enrich assignee with company/role, but keep this as optional metadata only.
Approval flow:

GET /tasks/pending → list pending items grouped by source_id.
UX flow (handled by client or CLI, but backend must support):
Show numbered list of tasks.
Accept input indicating:
“all”, “none”, or selected IDs (e.g., [1,3,5]).
Editing:
PATCH /tasks/:id to modify title/description/due date before creation.
Confirm:
POST /tasks/approve with selected ids.
Task creation:

Once approved:
Call task manager adapter:
For Todoist: use REST API to create tasks with title, description, due date, project.
Update status:
created if API success + store task_external_id.
failed if API errors; leave for retry.
Retry mechanism:
Simple CLI or endpoint POST /tasks/retry-failed.
Direct task creation:

For tasks/direct:
Use a small LLM or parser to extract:
Task description.
Due date/time (natural language parsing).
Confirm with user (or parameter autoCreate: boolean).
Create directly in the task manager (skip extraction pipeline but still log to DB with source_type = "direct").
Output:

When tasks are created, respond with:
tasks: [{ id, description, status, external_link }].
Take inspiration from OpenClaw meeting-followup automations, but implement a conservative, approval-gated pipeline that never auto-creates tasks without confirmation.

1.3s
expand each of these with a goal of automated zero trust framework

1) Personal CRM Intelligence — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are a senior backend engineer.
Implement a local-first personal CRM ingestion + scoring service with the following requirements, expanded to incorporate an automated Zero Trust framework. This means enforcing continuous verification of all data flows, least privilege access, automated threat detection, and assuming potential breaches in every interaction. Automation should handle real-time monitoring, anomaly detection, and policy enforcement without manual intervention.

Tech stack and basics:

Use either: (a) Python 3.11 + FastAPI + SQLAlchemy, or (b) Node 20 + TypeScript + Express + Prisma.
Persistence: SQLite database file personal_crm.db with WAL mode and foreign keys enabled.
All secrets (Gmail, Google Calendar, LLM API keys) from environment variables, managed via a secrets vault (e.g., HashiCorp Vault or env with encryption).
Provide a minimal CLI wrapper (e.g. python -m app ingest or node cli.js ingest) for cron.
Zero Trust Integration (Automated):

Authentication & Authorization: All API calls (e.g., Gmail, Calendar, LLM) must use short-lived tokens with automated rotation every 15 minutes via a background service. Implement JWT-based internal auth for CLI/HTTP endpoints, verifying caller identity on every request.
Data Encryption: Encrypt all sensitive fields (emails, names) at rest in SQLite using SQLCipher or equivalent. Automate key rotation quarterly. In-transit data uses TLS 1.3 enforced via middleware.
Least Privilege: Run ingestion processes in isolated containers (e.g., Docker) with minimal permissions (read-only for sources). Use role-based access control (RBAC) for database operations, automated via Prisma/SQLAlchemy hooks.
Continuous Verification: Implement automated anomaly detection: monitor ingestion volumes; if exchanges > 2x average, trigger alert and pause. Use ML-based (e.g., isolation forest via scikit-learn) to flag unusual contact patterns, integrated as a post-ingestion hook.
Audit Logging: Log every access/action to an immutable append-only log (e.g., JSONL with hashing), with automated rotation and offsite backup. Include threat indicators (e.g., IP, timestamp).
Assume Breach: Automate data segmentation: store PII in a separate encrypted table. Implement automated canary tokens in fake contacts to detect exfiltration.
Data sources and ingestion:

Email:
Integrate with Gmail via Gmail API or IMAP (configurable via env).
Scan the last 60 days of messages.
Extract: thread id, message id, date, sender, recipients, subject, snippet/body preview.
For each contact candidate, compute:
total_messages in threads involving that email.
thread_count involving that email.
exchange_count = Math.min(Math.floor(total_messages / 2), thread_count).
Calendar:
Integrate with Google Calendar API.
Scan the last 60 days of events.
Only include meetings with 1–10 attendees and duration ≥ 15 minutes.
Extract: start/end times, attendees (name + email), title.
Filtering (two-stage):
Stage 1 — hard filters:

Maintain learning.json config file with:
skip_domains: string[]
prefer_titles: string[]
skip_keywords: string[]
min_exchanges: number (default 1)
max_days_between: number (default 60)
max_attendees: number (default 10)
min_duration_minutes: number (default 15)
Always reject:
My own email addresses/domains (config: own_emails[], own_domains[]).
Emails in an explicit skip_contacts[] list.
Contacts already in CRM or previously rejected.
Generic inboxes: addresses starting with info@, team@, partnerships@, collabs@, noreply@.
Domains or local parts matching marketing patterns: prefixes noreply., tx., cx., mail., email..
Contacts with exchange_count < learning.min_exchanges.
Stage 2 — LLM-based classification:
For each remaining candidate, call a cheap LLM (Gemini Flash / Claude Haiku / GPT-4o-mini is fine; make the provider pluggable).
Prompt input fields:
name
email
exchange_count
list of sample subject lines (up to 5)
short snippets/body previews (up to 5)
LLM must respond with strict JSON: { "decision": "APPROVE" | "REJECT", "reason": "string" }.
Apply rules:
REJECT clearly automated or notification-only senders.
REJECT if all sample subjects look like newsletters/digests/reports.
REJECT cold outreach with low engagement (many one-way promos, low replies).
REJECT repetitive promo content (product launches, features, affiliate reports).
APPROVE only for genuine, two-way or meaningful relationships.
Treat malformed JSON from the LLM as REJECT with logged warning.
Contact model, scoring, and dedupe:
Database schema (simplified, adjust types to ORM):

contacts:
id (PK)
name
primary_email
emails (JSON array of strings)
company
role
context (short free-text)
last_touch_at (datetime)
contact_score (integer)
source_flags (JSON: { email: boolean, calendar: boolean })
tags (JSON array of strings)
created_at, updated_at
interactions:
id (PK)
contact_id (FK → contacts.id)
kind ("email" | "meeting")
external_id (e.g. Gmail thread ID or Calendar event ID)
occurred_at (datetime)
metadata (JSON)
rejections:
id (PK)
email
reason
domain
llm_reason
created_at
Add indexes on contacts.primary_email, contacts.name, and interactions.contact_id.
Deduplication rules:

When adding or updating a contact:
First search for an existing contact by exact primary_email.
If not found, search by any email in emails array.
If still not found, search by name + company combination (case-insensitive).
Merge records rather than creating duplicates:
Union of email sets.
Update last_touch_at to max of all.
Merge tags and source flags.
For rejections, track domain for each entry; when I explicitly reject a contact later (via some admin UI or CLI), add their domain to learning.skip_domains if it’s not my own domain.
Contact scoring (at each ingestion run, recompute):

Base score: 50.
+5 per email exchange (max +20).
+3 per meeting (max +15).
+15 if title matches learning.prefer_titles (e.g., "CEO", "Founder", "VP", "Head of", "Engineer", "Partner" — seeded defaults).
+10 if they appeared in small meetings (≤3 attendees) at least once.
Recency bonus:
+10 if last_touch_at within 7 days.
+5 if within 30 days.
+25 if person appears in both email and calendar.
+10 if they have a recognizable role (non-empty) and +5 if company is non-empty.
Semantic retrieval:

Create an embeddings table:
contact_embeddings:
contact_id (PK, FK → contacts.id)
embedding (BLOB)
embedding_dim (int)
provider (e.g. "google", "openai")
model
updated_at
Generate one embedding per contact from a concatenation of:
name, emails, company, role, top interaction subjects, and recent interaction snippets; max ~1000 chars.
Use either:
A vector extension (sqlite-vec) for cosine similarity, or
Load all embeddings into memory and compute cosine similarity in app code for now.
Add a retrieval function/API that lets me ask:
“Who did I meet from [company] last month?”
“When did I last talk to [name]?”
“Show contacts I haven’t spoken to in 30+ days.”
Implement these via a mix of:
Semantic similarity over embeddings for free-text queries.
Simple SQL filters on interactions and contacts.last_touch_at.
Notifications:

After each ingestion run (invoked via CLI), compute:
new_contacts_count
merged_contacts_count
rejected_contacts_count
any errors
Print a human-readable summary to stdout and optionally send a webhook or email summary (add a simple notifier interface).
Log detailed ingestion stats to a JSONL log file.
Deliverables:

Fully working service with:
CLI commands:
ingest-email
ingest-calendar
ingest-all (both + scoring + notifications)
HTTP endpoints (optional but preferred):
GET /contacts?query=... (search by text, uses semantic + basic filters)
GET /contacts/:id
POST /contacts/:id/reject (updates learning.json when appropriate)
Include a short README.md with:
Setup instructions,
How to configure Gmail/Calendar,
Example cron line for daily ingestion.
Use best practices: retry transient API failures, structured logging, and clear separation of providers (email/calendar/LLM) from the domain logic. Use the OpenClaw/OpenAI/Gemini LLM examples and SQLite+vector patterns from SQLite RAG guides as inspiration, but write all code from scratch. Ensure Zero Trust automation via scripts for token rotation, anomaly alerts (e.g., via webhook to monitoring tool), and periodic security scans.

2) Personal Knowledge Base (RAG) — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are a backend/infra engineer.
Implement a personal RAG knowledge base with URL/file ingestion, SQLite storage, and embedding-based retrieval, expanded to incorporate an automated Zero Trust framework. This includes automated access controls, data integrity checks, and threat monitoring for all ingestion/retrieval paths.

Tech & storage:

Language: Python 3.11 preferred (FastAPI + Pydantic + SQLAlchemy), but Node 20 + TS is acceptable.
Database: SQLite kb.db with WAL mode and foreign keys.
Two tables:
sources(id, url, title, source_type, summary, raw_content, content_hash UNIQUE, tags JSON, created_at, updated_at)
chunks(id, source_id FK, chunk_index, content, embedding BLOB, embedding_dim, embedding_provider, embedding_model, created_at)
Indexes:
CREATE INDEX idx_chunks_source_id ON chunks(source_id);
CREATE INDEX idx_sources_source_type ON sources(source_type);
CREATE UNIQUE INDEX idx_sources_content_hash ON sources(content_hash).
Zero Trust Integration (Automated):

Authentication & Authorization: Require API keys for all endpoints, validated via automated token service (e.g., JWT with auto-expiry). External APIs (e.g., FxTwitter, YouTube) use per-call auth with automated credential rotation.
Data Encryption: Encrypt raw_content and embeddings at rest; automate decryption only in memory during queries.
Least Privilege: Ingestion runs in sandboxed environments; retrieval queries are rate-limited and scoped to user-owned data.
Continuous Verification: Automate hash verification on ingestion to detect tampering; use anomaly detection on query patterns (e.g., sudden spike in retrievals triggers lockdown).
Audit Logging: Every ingestion/retrieval logged with automated analysis for suspicious activity (e.g., via simple rule-based engine).
Assume Breach: Segment data by sensitivity; automate data expiration for old chunks and canary insertions to detect unauthorized access.
Source type detection:

Accept:
URLs (web articles, YouTube, Twitter/X, other)
Files: PDFs, text, markdown.
Classify type by pattern:
Twitter/X: twitter.com, x.com.
YouTube: youtube.com, youtu.be.
PDFs by .pdf extension.
Else: article or text depending on input.
Content extraction (fallback chain):
For Twitter/X URLs:

Try FxTwitter API for tweet text, author, metadata (no auth).
If that fails, fall back to official X API (stub provider with env-based token).
If that fails, fall back to HTTP fetch + HTML parsing.
For YouTube URLs:
Try YouTube transcript extraction via:
Either official YouTube Data API, or
yt-dlp or similar library to fetch transcript/captions.
Extract: title, transcript text, (optionally) chapters.
For all other URLs:
Try a “readability” style extractor (e.g., Mozilla Readability port, or trafilatura/readability-lxml in Python).
If content is too short or looks like boilerplate, fall back to:
Firecrawl/Apify API (configure via env) OR a generic scraping provider.
If still failing or JS-heavy, fall back to headless browser (Playwright/Puppeteer) and re-run readability on rendered HTML.
Last resort: raw HTTP fetch + HTML tag stripping.
For files:
PDFs: use a robust PDF text extractor (e.g. pdfminer.six, pymupdf, or pdfplumber).
Plain text / markdown: treat file content as raw_content.
Retries:

For network calls, retry once on transient errors (ECONNRESET, ETIMEDOUT, DNS failures) with ~2-second delay.
Content quality validation:

Reject ingestion if:
Total content < 20 characters.
For non-tweets:
Total content < 500 characters.
Or < 15% of non-empty lines are longer than 80 characters (indicates mostly nav/boilerplate).
If page text contains at least 2 signals of error: "access denied", "captcha", "please enable javascript", "cloudflare", "404", "sign in", "blocked", "rate limit".
Truncate any content beyond 200,000 characters.
Deduplication:

URL normalization:
Strip tracking params: utm_*, fbclid, igshid, ref, s, t.
Normalize www..
Normalize twitter.com → x.com.
Remove trailing slashes and fragments.
Content hash:
Compute SHA-256 of cleaned content.
Store as content_hash and enforce UNIQUE.
If a source with same hash exists, skip creating a duplicate.
Chunking:

Chunk size: 800 characters.
Overlap: 200 characters between chunks.
Minimum chunk size: 100 characters; append small remainders to previous chunk.
Split on sentence boundaries using regex (?<=[.!?])\s+.
Store chunk_index starting at 0.
Embedding generation:

Use:
Google gemini-embedding-001 (768-dim) as primary, or
OpenAI text-embedding-3-small (1536-dim) as fallback.
Max input per chunk: 8000 characters (well above our chunk size).
Process in batches of 10 chunks with 200ms delay between batches to avoid rate limits.
Retry failed embedding calls up to 3 times with exponential backoff (1s, 2s, 4s).
Implement an in-memory LRU cache (size 1000) keyed by (provider, model, content_hash) to avoid re-embedding identical content.
Concurrency protection:

Use a lockfile (e.g. .kb_ingest.lock):
On ingestion start, create lock with PID and timestamp.
If lock exists:
If PID alive and lock age < 15 minutes → abort new ingestion.
If PID dead or lock age ≥ 15 minutes → treat as stale and override.
Retrieval:

HTTP endpoint: POST /query with { "question": string, "max_sources": number }.
Steps:
Embed query with same provider/model as chunks.
Run cosine similarity search over all chunk embeddings:
Either with SQLite-vec / vector extension, or in-memory compute.
Return top 10 chunks.
Deduplicate by source_id: keep best chunk per source.
For each selected chunk, sanitize content to max 2500 characters (center around high-density parts if you want; simple truncation is ok).
Call an LLM (configurable, e.g. local via OpenAI-compatible endpoint) with system prompt:
“You are a helpful assistant. Answer the user’s question using only the provided context chunks. If the answer is not in the context, say you don’t know. For each assertion, cite the source id(s) used.”
Return:
answer (string)
sources array with:
source_id
url
title
source_type
excerpt (chunk content substring)
CLI/HTTP:

Provide:
CLI: ingest-url <url>, ingest-file , query "<question>".
HTTP: POST /ingest/url, POST /ingest/file (multipart), POST /query.
Draw from SQLite RAG implementations that use SQLite-vec / sqlite-ai, but implement your own simplified layer around SQLite for storing BLOB embeddings and doing cosine similarity in app code if the vector extension is not used. Integrate Zero Trust via automated scripts for encryption key management, access audits, and breach simulation tests.

3) Content Idea Pipeline — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are implementing a content idea dedupe + task creation pipeline, expanded to incorporate an automated Zero Trust framework. This ensures all idea flows are verified, access is minimized, and threats are automatically detected/monitored.

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite content_ideas.db.
External systems (optional adapters):
X/Twitter search via some data provider.
Personal KB (Use Case #2) via HTTP API.
Project management tool (Asana / Linear / Notion / Todoist) via env-configured adapter.
Zero Trust Integration (Automated):

Authentication & Authorization: All endpoints require multi-factor tokens; automate verification with each call. External integrations use API keys with auto-rotation.
Data Encryption: Encrypt summaries and embeddings; automate decryption for processing only.
Least Privilege: Dedupe and task creation run in isolated modules with read-only DB access where possible.
Continuous Verification: Automate similarity score audits; flag and quarantine ideas with unexpected high matches.
Audit Logging: Log all pipeline steps with automated anomaly scanning (e.g., frequent duplicates trigger alerts).
Assume Breach: Automate data watermarking in briefs to track leaks; segment DB by status for quick isolation.
Schema:

ideas:
id (TEXT, PK, format YYYY-MM-DD-NNN)
date, type (short/long-form), title, slug (URL-friendly, UNIQUE)
summary (the pitch text)
tags (comma-separated)
status: pitched, accepted, rejected, produced, duplicate
response (my feedback)
embedding (BLOB)
created_at
Trigger / API:

When user submits a new idea via chat or HTTP:
Input: { idea_text: string, type?: "short" | "long", tags?: string[] }.
Convert to canonical summary (light rewrite allowed).
Generate a default title if not provided (short but descriptive).
Generate a slug (lowercase, hyphenated, dedup by appending -2, -3, etc. if needed).
Step 1 — Research:

Given idea_text, run:
Twitter/X search (if configured): recent tweets, last ~7–30 days.
Internal KB search (Use Case #2) via HTTP query.
Optional web search via any provider.
Store research refs (URL + snippet) in a transient structure for the brief, not in DB (or in a side table if needed).
Step 2 — Semantic dedupe (critical gate):

Compute an embedding for idea_text using Gemini/OpenAI.
For all existing ideas:
Semantic similarity (70% weight): Cosine similarity between the new idea's embedding and all stored embeddings.
Keyword matching (30% weight): Match against title (30%), summary (20%), and tags (20%).
Combined score: (semantic × 0.7) + (keyword × 0.3)
Hard gate: If any existing idea scores above 40% combined similarity, REJECT the new idea. Show me what it matched and the similarity score. No exceptions — this prevents re-pitching old concepts.
Step 3 — Brief assembly (only if it passes dedupe):

Keep it short — a few sentences on the angle and why it's video-worthy.
Do NOT generate titles, thumbnails, hooks, or scripts unless I explicitly ask.
Include links to relevant tweets or KB sources from the research step.
Step 4 — Task creation:

Create a task in my project management tool (Asana, Linear, Notion, or Todoist) with the brief as the description.
Step 5 — Store the idea:

Add the new idea + its embedding to the pitch database so future ideas are compared against it.
Set initial status to "pitched".
Step 6 — Notify me with the brief and a link to the created task.

Base the similarity + embedding logic on established RAG/SQLite vector patterns as in the RAG + SQLite material and adapt to this idea-table schema. Ensure Zero Trust through automated policy enforcement in code (e.g., hooks for logging/verification).

4) Social Media Research System (Cost-Optimized) — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are a backend engineer.
Implement a social media research tool that answers "What are people saying about [topic]?" using X/Twitter, while minimizing API costs, expanded with automated Zero Trust to verify all data sources and access.

Tech & config:

Language: Python 3.11 or Node 20 + TS.
No DB required initially; cache files in local disk (optionally SQLite if cheaper).
Config via env: Tier 2/3 keys, cache dir.
Zero Trust Integration (Automated):

Authentication & Authorization: Tiered APIs use per-request tokens; automate validation and rotation.
Data Encryption: Encrypt cached tweets; automate key management.
Least Privilege: Searches run with minimal scopes; automate query sanitization to prevent injection.
Continuous Verification: Automate content hashing to detect API tampering; flag unusual engagement patterns.
Audit Logging: Per-tier logs with automated threat scoring (e.g., high-cost spikes alert).
Assume Breach: Automate data minimization (delete caches after TTL); use honeypot queries for detection.
Query decomposition:

Take my question and break it into 2-4 focused search queries covering different angles.
Tiered retrieval — always try the cheapest option first:

Tier 1 (free): FxTwitter API (api.fxtwitter.com) — works for individual tweet lookups, no auth needed.
Tier 2 (low-cost, ~$0.15/1K tweets): A paid Twitter data provider like TwitterAPI.io or SocialData. Supports search, profiles, user tweets, and thread context.
Tier 3 (expensive, ~$0.005/tweet): Official X API v2 (api.x.com/2). Last resort only. Rate limit to 350ms between requests to stay under 450 req/15min.
How the tiers cascade by operation:

Single tweet lookup: Tier 1 → Tier 2 → Tier 3
Search: Tier 2 → Tier 3
Profile lookup: Tier 2 → Tier 3
Thread expansion: Tier 2 → Tier 3
Filtering:

Filter by timeframe (default: last 7 days, configurable).
Rank by engagement (likes + retweets + replies).
Remove retweets and duplicate content.
Suppress low-quality or spam results.
Thread expansion: For high-engagement tweets that are part of a thread, pull the full thread.

Caching: Cache results with a 1-hour TTL so repeated queries don't burn API credits.

Usage logging: Log every API call to a separate log file per tier, with timestamps and estimated costs.

Output — synthesize into a briefing:

Key narratives: 3-5 dominant takes
Notable posts: 5-10 most relevant tweets with links
Sentiment summary: positive, negative, or mixed
Contrarian takes: interesting minority opinions
Implement as standalone; integrate Zero Trust via automated scripts for logging analysis and access controls.

5) YouTube Analytics + Competitor Tracking — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are a data engineer.
Implement a YouTube analytics system that tracks my channel daily and monitors competitors, expanded with automated Zero Trust for secure data handling.

Tech & storage:

Language: Python 3.11.
Use YouTube Data API + YouTube Analytics API.
Database: SQLite youtube_analytics.db.
Charts: matplotlib (dark theme), PNG output.
Zero Trust Integration (Automated):

Authentication & Authorization: API calls use OAuth with auto-refresh; verify scopes per request.
Data Encryption: Encrypt metrics at rest; automate access logging.
Least Privilege: Collection jobs run isolated; queries limited to owned channels.
Continuous Verification: Automate data integrity checks (e.g., hash metrics); detect anomalous subscriber spikes.
Audit Logging: Log all collections with automated retention policies.
Assume Breach: Automate data partitioning by channel; use synthetic data for testing breaches.
Schema:

daily_stats: video_id, date, views, watch_time_minutes, avg_view_duration, impressions, ctr, subscribers_gained
videos: id, title, published_at, thumbnail_url, duration_seconds
Derived metrics: 7-day moving average for daily views, etc.
Competitor tracking:

Configurable list of competitor channel IDs.
Daily pull: recent uploads, subscriber count.
Chart generation: Produce PNG charts as specified.

Schedule: Daily cron job. Ensure Zero Trust via automated verification in collection scripts.

6) Nightly Business Briefing (Multi-Perspective AI Council) — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are building a nightly business analysis system, expanded with automated Zero Trust for secure signal processing.

Tech:

Language: Python 3.11.
LLM provider: configurable.
Storage: SQLite business_council.db.
Zero Trust Integration (Automated):

Authentication & Authorization: Signal adapters require verified sources; automate LLM call auth.
Data Encryption: Encrypt signals; automate in-memory processing.
Least Privilege: Personas run in isolated threads; limit data access.
Continuous Verification: Automate recommendation validation against signals; flag inconsistencies.
Audit Logging: Full council trace logged immutably with automated review.
Assume Breach: Automate signal sanitization; use differential privacy for aggregates.
Signal collection: Pull from tools, normalize to JSON.

Three-phase AI council: As specified, with phases using frontier models.

Ranking: Priority formula with weights.

Filter: Remove "publish now" recs.

Output: Ranked recs, stored trace. Schedule nightly. Integrate Zero Trust via automated hooks for verification/logging.

7) CRM/Business Tool Natural Language Access — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are implementing a natural-language interface to my CRM, expanded with automated Zero Trust for secure operations.

Tech:

Language: Node 20 + TypeScript or Python 3.11.
Integrate with HubSpot first, pluggable.
Zero Trust Integration (Automated):

Authentication & Authorization: NL queries require user auth; automate CRM API token rotation.
Data Encryption: Encrypt query results; automate PII masking.
Least Privilege: Operations scoped to minimal fields; automate query auditing.
Continuous Verification: Automate intent classification checks; reject anomalous queries.
Audit Logging: Log all interactions with automated threat detection.
Assume Breach: Automate rate limiting and data exfiltration monitoring.
Supported objects: Contacts, Companies, Deals, etc.

Intent classification: Parse to lookup/create/update/etc.

Validation: Ask for missing fields.

Response: Clean summaries. Ensure Zero Trust via automated controls in the pipeline.

8) AI Content Humanization — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are implementing a text rewriting tool, expanded with automated Zero Trust for content security.

Tech:

Language: Python or Node.
Use cheap LLM.
Zero Trust Integration (Automated):

Authentication & Authorization: API calls auth'd; automate input sanitization.
Data Encryption: Encrypt text in transit/storage.
Least Privilege: Rewrite isolated from other data.
Continuous Verification: Automate detection of malicious patterns in input.
Audit Logging: Log rewrites with automated review for biases.
Assume Breach: Automate watermarking in output to track usage.
Input: Draft text.

Step 1 — Detection: Scan for AI tells.

Step 2 — Rewrite: As specified, with channel tuning.

Output: Revised text. Integrate Zero Trust via automated filters in code.

9) Image Generation + Iterative Editing — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are implementing an image generation workflow, expanded with automated Zero Trust for asset security.

Tech:

Language: Node 20 + TS or Python.
Provider: pluggable (DALL-E, etc.).
Zero Trust Integration (Automated):

Authentication & Authorization: Sessions auth'd; automate provider token rotation.
Data Encryption: Encrypt images/metadata at rest.
Least Privilege: Edits run in sandboxes.
Continuous Verification: Automate prompt scanning for prohibited content.
Audit Logging: Log iterations with automated anomaly detection.
Assume Breach: Automate image watermarking and expiration.
Core flow: Describe, generate variants, iterate, edit.

Context tracking: Session state.

Output: Save finals. Ensure Zero Trust via automated security in workflow.

10) Task Management from Meetings + Chat — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are building a task extraction system, expanded with automated Zero Trust for action item security.

Tech:

Language: Python 3.11 or Node 20 + TS.
Task manager adapter.
Zero Trust Integration (Automated):

Authentication & Authorization: Inputs auth'd; automate approval gating.
Data Encryption: Encrypt transcripts/tasks.
Least Privilege: Extraction isolated; CRM cross-ref scoped.
Continuous Verification: Automate vagueness checks; flag suspicious assignees.
Audit Logging: Log extractions/approvals with automated audits.
Assume Breach: Automate pending state monitoring for tampering.
Input sources: Transcripts, notes, commands.

Extraction: Use LLM for action items.

Approval flow: Show, select, edit, confirm.

Task creation: Via API, with defaults. Integrate Zero Trust via automated verification in flow.

11) AI Usage and Cost Tracking — Implementation Prompt (Expanded with Automated Zero Trust Framework)
You are implementing a centralized AI usage logger + cost reporter, expanded with automated Zero Trust for tracking security.

Core design:

Library for wrapping LLM calls.
Log to JSONL; optional SQLite mirror.
Zero Trust Integration (Automated):

Authentication & Authorization: Wraps require auth; automate key checks.
Data Encryption: Encrypt logs; automate rotation.
Least Privilege: Reporting queries limited.
Continuous Verification: Automate cost estimate validation; flag overages.
Audit Logging: Meta-log usage of the tracker itself.
Assume Breach: Automate log tampering detection via hashing.
Logging structure: Timestamp, model, tokens, etc.

Cost calculation: Pricing table.

Reporting CLI: Summaries by filters.

Routing suggestions: Flag optimizations. Integrate Zero Trust via automated scripts for log integrity and alerts.

979ms
anything missing? suggestions? use cases?

Yes, the expansions are quite comprehensive and well-aligned with core Zero Trust principles ("never trust, always verify", least privilege, continuous verification, assume breach). You've covered the major pillars for a personal/local-first automation system: strong encryption, short-lived credentials, anomaly detection, audit trails, data segmentation, and automated policy enforcement/rotation.

That said, there are some gaps, refinements, and emerging best practices (especially relevant in 2026 with AI agents, local LLMs, and self-hosted tools) that could make the framework even more robust, automated, and future-proof.

Anything Missing? (Key Gaps & Additions)
Machine Identity for AI Agents & Local Tools
Many use cases now involve local AI (e.g., Ollama, local Grok-like models, or agentic workflows in #6 council, #9 image gen, #10 task extraction).
Current expansions treat LLMs as "cheap/fast models" but don't give them unique, verifiable identities (cryptographic machine identities, short-lived certs/tokens).
Missing: Automated issuance/rotation of workload identities (e.g., via SPIFFE/SPIRE open-source or lightweight equivalent) so each agent/component verifies itself before accessing DBs, APIs, or external services.
Suggestion: Add a "Workload Identity Provider" module (self-hosted, e.g., based on OpenZiti or small SPIFFE impl) that issues per-session JWTs or mTLS certs to local processes/LLM calls.
Policy-as-Code & Automated Enforcement Engine
You have good per-component policies, but no central automated decision point.
Missing: A unified policy engine (e.g., Open Policy Agent / OPA or Cerbos open-source) that evaluates every request across all use cases (ingestion, retrieval, task creation, council phases).
This allows writing policies once (in Rego/YAML) like: "LLM calls to external APIs only if input hash matches expected pattern AND device posture OK".
Automate drift detection: nightly scans compare running config vs policy repo.
Post-Quantum Readiness & Cryptographic Agility
2026 predictions emphasize PQC (post-quantum crypto) for long-lived data/machine identities.
Current: Good use of TLS 1.3 + encryption at rest, but add crypto-agility (automated fallback to Kyber/Dilithium hybrids via libs like OpenQuantumSafe).
Especially relevant for stored embeddings, contact PII, task logs that live years.
Device/Posture Checks for Local-First Execution
Since this is local/self-hosted (SQLite on personal machine/server), add automated checks:
TPM/secure enclave attestation for the host running ingestions/LLMs.
Endpoint posture (e.g., OS patch level, no known malware via local EDR hooks like osquery).
Block ingestion if device fails checks (e.g., via small agent or script).
Data Lineage & Provenance Tracking
For RAG (#2), council (#6), humanization (#8): track where data came from (provenance) and how it was transformed.
Missing: Append cryptographic proofs (e.g., simple Merkle proofs or Sigstore-style signing) to chunks/recommendations so you can audit "was this output influenced by tampered source?".
Automated Breach Simulation & Red Teaming
"Assume breach" is mentioned, but make it active:
Monthly automated canary/poisoned-data tests (inject fake PII or bad embeddings → detect if leaked/used).
Auto-quarantine on detection.
Observability & AI-Specific Monitoring
Add Prometheus + Grafana (or lightweight equivalent) for metrics: token usage, anomaly scores, policy violations.
LLM-specific: monitor prompt injection attempts, hallucination rates (via self-consistency checks), excessive agency in agents (#10 tasks, #6 council).
Suggestions / Refinements
Central Zero Trust Gateway
Instead of scattering auth/encryption in every use case, funnel external calls (Gmail, YouTube, X APIs, LLM providers) through one local proxy/gateway (e.g., inspired by Pomerium, OpenZiti, or tiny self-hosted ZTNA like FerrumGate). This enforces uniform mTLS, logging, rate-limiting, and PII redaction.
Secretless Architecture
Move away from env vars for API keys → use short-lived credentials fetched from local Vault/Infisical or workload identity. Automate rotation every 1–24h.
Immutable Logs & Tamper Detection
Use append-only structure + hash chaining (or lightweight blockchain-like Merkle tree) so you can prove no tampering occurred.
User-Friendly Feedback Loop
After each run (ingest, council, etc.), send encrypted summary + "security posture score" (e.g., "98% — 2 anomalies detected & quarantined") via preferred channel.
Prioritize Open-Source ZT Building Blocks
Network/Access: OpenZiti or NetBird (open-source ZT overlay)
Policy: OPA/Cerbos
Identity: Small SPIFFE impl or step-ca
Proxy: Pomerium (self-hosted ZTNA)
These integrate well with local-first setups.
Additional / Enhanced Use Cases
Here are a few new or extended use cases that naturally fit your OpenClaw-style personal automation while deeply embedding automated Zero Trust:

Secure Local AI Agent Orchestrator
A central dispatcher that runs local agents (e.g., research → dedupe → task) with per-agent identities, just-in-time permissions, and automatic revocation on anomaly.
PII & Sensitive Data Vault
Dedicated encrypted store (separate SQLite + SQLCipher) for all PII (contacts, transcripts); every use case must request ephemeral read access via policy engine.
Automated Security Posture Dashboard
Nightly cron that scans all components (DB integrity, token ages, anomaly logs) → generates simple report + auto-remediates low-risk issues (e.g., rotate expired certs).
Adversarial Prompt Defense Layer
For all LLM calls (#2 RAG, #6 council, #8 humanize, #10 extraction): insert automated guardrail (prompt filters + output scanning) + Zero Trust check (only allow if caller identity matches expected agent).
Cross-Use-Case Data Flow Auditing
Track lineage across tools: "this council recommendation used data from CRM #1 + KB #2 ingested from X search #4" with verifiable proofs.
Overall, you're already 80–85% there — the biggest uplift comes from centralizing policy/identity (one enforcement point) and adding machine identity + provenance for the AI parts, which are becoming the highest-risk surface in personal automation stacks in 2026.

If you want, I can expand any of the existing 1–11 prompts further with one of these additions, or draft a new #12 "Zero Trust Orchestrator" use case. What direction interests you most?

2.4s
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expand all of the existing 1–11 prompts further with these additions and create new #12 "Zero Trust Orchestrator" use case

1) Personal CRM Intelligence — Implementation Prompt (Further Expanded with Additions)
You are a senior backend engineer.
Implement a local-first personal CRM ingestion + scoring service with the following requirements, further expanded to incorporate advanced automated Zero Trust elements: machine identities for AI agents (e.g., LLM classifiers), policy-as-code enforcement, post-quantum crypto agility, device posture checks, data lineage tracking, automated breach simulation, observability/AI monitoring, a central ZT gateway for external calls, secretless architecture, immutable logs with tamper detection, user-friendly feedback, and open-source ZT tools (e.g., OpenZiti for networking, OPA for policies, step-ca for identities).

Tech stack and basics:

Use either: (a) Python 3.11 + FastAPI + SQLAlchemy, or (b) Node 20 + TypeScript + Express + Prisma.
Persistence: SQLite database file personal_crm.db with WAL mode and foreign keys enabled.
All secrets managed secretlessly via workload identities (e.g., fetched from local step-ca CA).
Provide a minimal CLI wrapper (e.g. python -m app ingest or node cli.js ingest) for cron.
Further Zero Trust Additions (Automated):

Machine Identity for AI Agents: Assign unique, short-lived certs (via step-ca) to LLM calls (e.g., classification in Stage 2); verify identity before processing. Automate rotation every 15min.
Policy-as-Code Enforcement: Use OPA/Cerbos to evaluate all operations (e.g., "allow ingestion if device posture OK and input volume < threshold"); central policy repo with auto-drift detection.
Post-Quantum Readiness: Use crypto-agile libs (e.g., OpenQuantumSafe) for encryption; default to hybrid Kyber + AES for PII fields.
Device/Posture Checks: Pre-ingestion, use osquery to verify host (e.g., patches up-to-date, no malware); block if failed.
Data Lineage & Provenance: Track each contact's origin with Merkle proofs (signed hashes of sources); store in a provenance table for audit.
Automated Breach Simulation: Monthly cron injects canary contacts; detect usage/exfiltration and auto-quarantine.
Observability & AI Monitoring: Export metrics (e.g., anomaly scores, LLM hallucinations via self-checks) to Prometheus; dashboard for "security posture score".
Central ZT Gateway: Route all external API calls (Gmail, Calendar, LLM) through local OpenZiti/Pomerium proxy for mTLS, logging, and PII redaction.
Secretless Architecture: No env vars; identities fetch creds dynamically from local CA.
Immutable Logs & Tamper Detection: Append-only JSONL with hash chaining; nightly verify integrity.
User-Friendly Feedback: Post-run, send encrypted summary + posture score (e.g., "95% secure — 1 anomaly auto-resolved") via webhook.
Data sources and ingestion:

Email: Integrate via Gmail API/IMAP through ZT gateway; scan last 60 days; extract as specified.
Calendar: Similar integration; filter meetings as specified.
Filtering (two-stage): As originally specified, with OPA policy checks on rejects/adds.

Contact model, scoring, and dedupe: Schema as specified; add provenance BLOB field for lineage. Dedupe with tamper-proof hashes.

Semantic retrieval: Embeddings with provenance; queries via policy-enforced API.

Notifications: Enhanced with posture feedback.

Deliverables: As specified, plus policy files, ZT gateway setup in README, and breach sim scripts. Use best practices; ensure all additions automate via hooks/crons.

2) Personal Knowledge Base (RAG) — Implementation Prompt (Further Expanded with Additions)
You are a backend/infra engineer.
Implement a personal RAG knowledge base with URL/file ingestion, SQLite storage, and embedding-based retrieval, further expanded with machine identities for embedding LLMs, policy-as-code, post-quantum agility, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless arch, immutable logs, user feedback, and open-source tools (OpenZiti, OPA, step-ca).

Tech & storage: As originally specified.

Further Zero Trust Additions (Automated):

Machine Identity: Certs for embedding/retrieval agents; verify before vector ops.
Policy-as-Code: OPA evaluates ingestion/retrieval (e.g., "deny if source untrusted or query anomalous").
Post-Quantum: Hybrid crypto for embeddings/content.
Device/Posture: osquery checks pre-op.
Data Lineage: Merkle-signed chunks; provenance table.
Breach Simulation: Inject poisoned chunks monthly; detect in queries.
Observability: Metrics on query anomalies, LLM consistency; Prometheus export.
Central ZT Gateway: Proxy all extractors (FxTwitter, YouTube, etc.).
Secretless: Dynamic cred fetch.
Immutable Logs: Hash-chained; integrity checks.
User Feedback: Post-query posture summaries.
Source type detection: As specified.

Content extraction: Fallback chain through gateway.

Content quality validation: With policy enforcement.

Deduplication: Hash with provenance.

Chunking & Embedding: As specified; add lineage signing.

Concurrency protection: Lock with device check.

Retrieval: Policy-gated; observability on results.

CLI/HTTP: Auth'd via identities. Draw from RAG guides; add ZT elements via integrated modules.

3) Content Idea Pipeline — Implementation Prompt (Further Expanded with Additions)
You are implementing a content idea dedupe + task creation pipeline, further expanded with machine identities for research/dedupe agents, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT (OpenZiti, OPA, step-ca).

Tech & storage: As originally specified.

Further Zero Trust Additions (Automated):

Machine Identity: Certs for embedding/similarity agents.
Policy-as-Code: OPA for dedupe gates (e.g., "reject if similarity >0.4 AND source trusted").
Post-Quantum: Crypto for embeddings.
Device/Posture: Checks before research.
Data Lineage: Provenance for ideas/briefs.
Breach Simulation: Inject duplicate ideas; detect handling.
Observability: Monitor dedupe anomalies.
Central ZT Gateway: For X/KB/web searches.
Secretless: For PM tool creds.
Immutable Logs: For pipeline steps.
User Feedback: Post-commit posture.
Schema: As specified; add provenance.

API: Policy-enforced.

Step 1 — Research: Through gateway.

Step 2 — Dedupe: With OPA.

Steps 3-6: As specified; lineage in briefs/tasks. Tests: Include ZT scenarios.

4) Social Media Research System (Cost-Optimized) — Implementation Prompt (Further Expanded with Additions)
You are a backend engineer.
Implement a cost-optimized X/Twitter research service, further expanded with machine identities for search agents, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech & config: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For tiered providers.
Policy-as-Code: OPA for query decomposition/filtering.
Post-Quantum: Crypto for caches/logs.
Device/Posture: Pre-search.
Data Lineage: Provenance for tweets/narratives.
Breach Simulation: Inject spam; detect suppression.
Observability: Cost/anomaly metrics.
Central ZT Gateway: For all tiers.
Secretless: API keys dynamic.
Immutable Logs: Per-tier with chains.
User Feedback: Briefing + posture.
Query decomposition: As specified; policy-gated.

Tiered retrieval: Through gateway.

Filtering/Ranking: With lineage.

Caching/Logging: Immutable.

Output: Enhanced with feedback. Standalone with ZT integrations.

5) YouTube Analytics + Competitor Tracking — Implementation Prompt (Further Expanded with Additions)
You are a data engineer.
Implement a YouTube analytics system, further expanded with machine identities for collection agents, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech & storage: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For API calls.
Policy-as-Code: OPA for data collection (e.g., "allow if channel owned").
Post-Quantum: Metrics encryption.
Device/Posture: Pre-cron.
Data Lineage: Provenance for metrics/charts.
Breach Simulation: Inject fake metrics; detect.
Observability: Trend anomalies.
Central ZT Gateway: For YouTube APIs.
Secretless: OAuth dynamic.
Immutable Logs: Collection logs.
User Feedback: Summary + posture.
Schema: Add provenance.

Data collection: Through gateway; policy-enforced.

Derived metrics/Charts: With lineage.

API/CLI: Auth'd. Guard rates; add ZT crons.

6) Nightly Business Briefing (Multi-Perspective AI Council) — Implementation Prompt (Further Expanded with Additions)
You are building an AI council pipeline, further expanded with machine identities for personas, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: Per-persona certs.
Policy-as-Code: OPA for signal validation/recommendation scoring.
Post-Quantum: Signal encryption.
Device/Posture: Pre-nightly.
Data Lineage: Provenance for recs (trace to signals).
Breach Simulation: Inject bad signals; detect in consensus.
Observability: Hallucination rates; metrics.
Central ZT Gateway: For adapters/LLM calls.
Secretless: Dynamic creds.
Immutable Logs: Council traces.
User Feedback: Output + posture.
Signal collection: Policy-gated; lineage.

Phases: As specified; with identities/observability.

Ranking/Filter: OPA-enforced.

Output: With feedback. Use multi-agent patterns; add ZT via LLM hooks.

7) CRM/Business Tool Natural Language Access — Implementation Prompt (Further Expanded with Additions)
You are implementing a natural-language interface to CRM, further expanded with machine identities for NL agents, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For intent/extraction LLMs.
Policy-as-Code: OPA for ops (e.g., "deny create if PII unmasked").
Post-Quantum: Response encryption.
Device/Posture: Pre-query.
Data Lineage: Provenance for results.
Breach Simulation: Inject bad queries; detect.
Observability: Query anomalies.
Central ZT Gateway: For CRM APIs.
Secretless: OAuth dynamic.
Immutable Logs: Interactions.
User Feedback: Response + posture.
Supported objects: As specified.

NL interface: Policy-gated pipeline.

Response: With lineage. Use NL-to-CRM practices; add ZT adapters.

8) AI Content Humanization — Implementation Prompt (Further Expanded with Additions)
You are implementing a text rewriting tool, further expanded with machine identities for rewrite LLM, policy-as-Code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For LLM calls.
Policy-as-Code: OPA for input/output scans (e.g., bias check).
Post-Quantum: Text encryption.
Device/Posture: Pre-rewrite.
Data Lineage: Provenance for revisions.
Breach Simulation: Inject adversarial text; detect.
Observability: Hallucination monitoring.
Central ZT Gateway: For LLM provider.
Secretless: Dynamic.
Immutable Logs: Rewrites.
User Feedback: Output + posture.
Steps: As specified; with policy/observability.

Output: With lineage. Build focused; add ZT filters.

9) Image Generation + Iterative Editing — Implementation Prompt (Further Expanded with Additions)
You are implementing an image generation workflow, further expanded with machine identities for gen/edit agents, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For provider calls.
Policy-as-Code: OPA for prompt scans (e.g., prohibit NSFW).
Post-Quantum: Image metadata encryption.
Device/Posture: Pre-session.
Data Lineage: Provenance for variants (trace to prompts).
Breach Simulation: Inject bad prompts; detect.
Observability: Iteration anomalies.
Central ZT Gateway: For image APIs.
Secretless: Dynamic creds.
Immutable Logs: Sessions.
User Feedback: Final + posture.
Core flow: As specified; policy-gated.

Context: With lineage. Keep abstract; add ZT in workflow.

10) Task Management from Meetings + Chat — Implementation Prompt (Further Expanded with Additions)
You are building a task extraction system, further expanded with machine identities for extraction LLM, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Tech: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For LLM/CRM calls.
Policy-as-Code: OPA for approval gating (e.g., "deny if task sensitive").
Post-Quantum: Task encryption.
Device/Posture: Pre-input.
Data Lineage: Provenance for tasks (trace to transcripts).
Breach Simulation: Inject vague items; detect rejection.
Observability: Extraction anomalies.
Central ZT Gateway: For task manager/CRM.
Secretless: Dynamic.
Immutable Logs: Approvals.
User Feedback: Confirmation + posture.
Input sources: As specified.

Extraction/Approval: Policy-enforced.

Task creation: With lineage. Conservative; add ZT gating.

11) AI Usage and Cost Tracking — Implementation Prompt (Further Expanded with Additions)
You are implementing a centralized AI usage logger + cost reporter, further expanded with machine identities for wrappers, policy-as-code, post-quantum, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless, immutable logs, user feedback, and open-source ZT.

Core design: As specified.

Further Zero Trust Additions (Automated):

Machine Identity: For LLM wraps.
Policy-as-Code: OPA for call allowances (e.g., "deny if over budget").
Post-Quantum: Log encryption.
Device/Posture: Pre-report.
Data Lineage: Provenance for estimates.
Breach Simulation: Inject high-cost calls; detect flags.
Observability: Spend anomalies.
Central ZT Gateway: For any external (if added).
Secretless: Dynamic.
Immutable Logs: Hash-chained.
User Feedback: Report + posture.
Logging/Cost: As specified.

Reporting: Policy-gated. Integrate ZT via log wrappers.

12) Zero Trust Orchestrator — Implementation Prompt
You are implementing a central Zero Trust Orchestrator as the backbone for all OpenClaw use cases (#1–11), automating machine identities, policy enforcement, gateway routing, secretless creds, device posture, data lineage, breach simulation, observability, immutable logs, post-quantum crypto, and user feedback using open-source tools (OpenZiti for networking, OPA for policies, step-ca for identities, osquery for posture, Prometheus for metrics).

Tech & storage:

Language: Python 3.11 + FastAPI (or Node/TS).
Storage: SQLite zt_orchestrator.db for identities/policies/logs; Prometheus for metrics.
Integrate as middleware/service that other use cases call into (e.g., via gRPC/HTTP).
Schema:

identities: id (PK), workload_name, cert (BLOB), expiry, rotated_at.
policies: id (PK), rule (Rego/YAML), target_use_case, last_checked.
logs: timestamp, event, hash (for chaining), provenance (JSON).
anomalies: id (PK), type, severity, auto_resolved (bool).
Core Components (Automated):

Machine Identity Provider: Use step-ca to issue/rotate certs for agents/LLMs/components; API: POST /identity/issue {workload: string} → short-lived cert.
Policy Engine: OPA/Cerbos; load policies from repo; API: POST /evaluate {context: JSON, action: string} → allow/deny + reasons. Enforce for all ops across use cases.
Central ZT Gateway: OpenZiti/Pomerium proxy; route external calls with mTLS, logging, redaction; auto-config edges for local services.
Secretless Creds: Fetch from CA; no static keys.
Device/Posture Checks: osquery integration; API: GET /posture → score; block ops if < threshold.
Data Lineage & Provenance: Merkle tree builder; API: POST /sign {data: JSON} → signed hash; store/verify chains.
Breach Simulation & Red Teaming: Cron: inject canaries/poisoned data into use cases; detect via monitors; auto-quarantine (e.g., rollback DB).
Observability & AI Monitoring: Prometheus exporter for metrics (anomalies, rotations, hallucinations); Grafana dashboard; alert on thresholds (e.g., high LLM variance).
Immutable Logs & Tamper Detection: Append-only with hash chaining; nightly integrity scan.
Post-Quantum Readiness: Hybrid crypto via OpenQuantumSafe in all signing/encryption.
User-Friendly Feedback: API: POST /report {use_case: string} → encrypted summary + posture score; send via webhook.
API/CLI:

CLI: zt-init (setup CA/gateway), zt-rotate-all, zt-simulate-breach, zt-dashboard (open Grafana).
HTTP: /orchestrate/{action} (e.g., issue identity, evaluate policy, sign data).
Integration: Each use case (#1–11) must wrap calls (e.g., ingestion: check posture → issue identity → evaluate policy → route through gateway → sign output).

Deliverables: Standalone service; README with setup (e.g., docker-compose for OpenZiti/OPA/step-ca); examples hooking into #1 CRM. Prioritize automation via crons/hooks for rotation/sims/audits.

1.5s
add a new number 13 which is a research framework for ArXiv and other scientific research APIs

13) Scientific Research Framework (ArXiv & APIs) — Implementation Prompt
You are a backend/research engineer.
Implement a scientific research framework that searches, ingests, and analyzes papers from ArXiv and other APIs (e.g., PubMed, Semantic Scholar, Crossref), with RAG-style retrieval for queries like "latest on quantum computing". Further expanded with machine identities for search/ingestion agents, policy-as-code, post-quantum agility, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless arch, immutable logs, user feedback, and open-source tools (OpenZiti, OPA, step-ca).

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite sci_research.db with WAL mode and foreign keys.
Tables:
papers: id (PK), title, authors (JSON), abstract, url, source_api, pdf_hash (UNIQUE), tags (JSON), ingested_at, updated_at.
chunks: id (PK), paper_id (FK), chunk_index, content, embedding (BLOB), embedding_dim, provider, model.
queries: id (PK), query_text, results (JSON), executed_at.
Indexes: On papers.source_api, chunks.paper_id.
External APIs: ArXiv (free), PubMed (NCBI E-utils), Semantic Scholar (API key), Crossref (free/public).
Further Zero Trust Additions (Automated):

Machine Identity: Certs (via step-ca) for API agents/embedding LLMs; verify before searches/ingests. Automate rotation every 15min.
Policy-as-Code: OPA/Cerbos evaluates ops (e.g., "allow search if query non-sensitive and posture OK"); central repo with auto-drift detection.
Post-Quantum Readiness: Hybrid crypto (OpenQuantumSafe) for hashes/embeddings/PDF storage.
Device/Posture Checks: osquery verifies host pre-op; block if failed.
Data Lineage & Provenance: Merkle-signed paper chunks; provenance table for audit (trace to API source).
Automated Breach Simulation: Monthly inject poisoned papers (e.g., altered abstracts); detect in retrievals and auto-quarantine.
Observability & AI Monitoring: Prometheus metrics on search volumes, embedding consistency (self-checks for hallucinations); dashboard for posture scores.
Central ZT Gateway: Route all API calls (ArXiv, PubMed, etc.) through OpenZiti/Pomerium for mTLS, logging, and content redaction (e.g., remove PII).
Secretless Architecture: Dynamic cred fetch from local CA; no env vars.
Immutable Logs & Tamper Detection: Append-only JSONL with hash chaining; nightly integrity verification.
User-Friendly Feedback: Post-query/ingestion send encrypted summary + posture score (e.g., "98% secure — no anomalies") via webhook.
API Surface:

POST /research/search: { query: string, apis?: ["arxiv", "pubmed", "semanticscholar", "crossref"], max_results?: 10 } → returns paper metadata + snippets.
POST /research/ingest: { paper_url: string or id: string } → downloads PDF, extracts text, chunks, embeds, stores.
POST /research/query: { question: string } → RAG retrieval over stored papers.
Step 1 — Search:

Decompose query into API-specific terms (e.g., ArXiv: categories like cs.AI; PubMed: MeSH terms).
Call APIs through ZT gateway:
ArXiv: Use arxiv.py lib or API for title/author/abstract/full-text links.
PubMed: E-utils for PMIDs, fetch abstracts/metadata.
Semantic Scholar: API for DOI-based searches, citations.
Crossref: For metadata/DOIs.
Limit: max_results per API; filter by date/relevance.
Policy: OPA checks query for sensitivity (e.g., reject medical PII).
Step 2 — Ingestion:

For selected papers: Download PDF via gateway; validate hash against source.
Extract text: Use pdfplumber/GROBID for structured content (title, abstract, sections).
Quality Validation: Reject if <500 chars or error signals (e.g., "paywall").
Deduplication: SHA-256 on abstract + title; skip if exists.
Chunking: 800 chars, 200 overlap, sentence splits.
Embedding: Gemini/OpenAI via agent identity; batch with retries.
Step 3 — Retrieval (RAG):

Embed query; cosine search chunks (sqlite-vec or in-memory).
Top 10 deduped; LLM synthesize: "Answer using context; cite papers."
Observability: Monitor for low-relevance results.
Step 4 — Analysis:

Optional: LLM for summaries, trends (e.g., "key advancements in field").
Lineage: Sign outputs with provenance.
Concurrency: Lockfile with posture check.

Deliverables:

CLI: search "<query>", ingest "<url>", query "<question>".
HTTP endpoints as above.
README: Setup APIs, ZT integrations (e.g., step-ca config), breach sim scripts.
Tests: Synthetic searches, ZT failures (e.g., bad posture blocks).
Inspire from academic search tools; ensure ZT automation via crons/hooks (rotations, sims, audits). Integrate with #2 KB for cross-tool RAG if configured.

662ms
update with extract all available formats ie pdf, html, ps, json, latex what ever formats each server is capable of exporting

Here is the updated #13) Scientific Research Framework (ArXiv & APIs) — Implementation Prompt, with enhanced ingestion and extraction logic to pull all available formats supported by each API/source (PDF, HTML, TeX/LaTeX source, JSON metadata, XML, PostScript/PS where applicable, and any others exposed). The system now attempts to fetch, validate, store hashes of, and optionally chunk/embed multiple formats per paper when available.

13) Scientific Research Framework (ArXiv & APIs) — Implementation Prompt (Updated with Multi-Format Extraction)
You are a backend/research engineer.
Implement a scientific research framework that searches, ingests, and analyzes papers from ArXiv and other APIs (e.g., PubMed/NCBI E-utils, Semantic Scholar, Crossref), with RAG-style retrieval for queries like "latest on quantum computing". The ingestion step must extract and store all available export formats supported by each source (PDF, HTML, TeX/LaTeX source, JSON metadata, XML, PostScript/PS where exposed, and any others). Further expanded with machine identities for search/ingestion agents, policy-as-code, post-quantum agility, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless arch, immutable logs, user feedback, and open-source tools (OpenZiti, OPA, step-ca).

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite sci_research.db with WAL mode and foreign keys.
Tables:
papers: id (PK), title, authors (JSON), abstract, doi (if available), arxiv_id (if arXiv), source_api, ingested_at, updated_at.
formats: id (PK), paper_id (FK), format_type (e.g., "pdf", "html", "tex_source", "json_metadata", "xml", "ps", "other"), url_or_path (stored file path or direct URL), content_hash (SHA-256 UNIQUE per format per paper), size_bytes, fetched_at, metadata (JSON e.g., version, status).
chunks: id (PK), paper_id (FK), format_type (references which format chunked), chunk_index, content, embedding (BLOB), embedding_dim, provider, model.
queries: id (PK), query_text, results (JSON), executed_at.
Indexes: On papers.source_api, formats.paper_id + format_type, chunks.paper_id.
Further Zero Trust Additions (Automated):

Machine Identity: Certs (via step-ca) for API agents/embedding LLMs; verify before searches/ingests/downloads. Automate rotation every 15min.
Policy-as-Code: OPA/Cerbos evaluates ops (e.g., "allow ingest format 'tex_source' only if policy permits source code execution risks"; "deny PDF if size > 50MB"). Central repo with auto-drift detection.
Post-Quantum Readiness: Hybrid crypto (OpenQuantumSafe) for all hashes, signatures, and stored content/embeds.
Device/Posture Checks: osquery verifies host pre-op; block if failed.
Data Lineage & Provenance: Merkle-signed per-format fetches; provenance table tracks API source, timestamp, headers, and chain of custody.
Automated Breach Simulation: Monthly inject altered format files (e.g., poisoned PDF); detect tampering via hash mismatches in retrievals and auto-quarantine.
Observability & AI Monitoring: Prometheus metrics on format fetch success rates, embedding consistency (self-checks for hallucinations), multi-format coverage; dashboard for posture scores.
Central ZT Gateway: Route all API calls and downloads (ArXiv PDF/HTML/TeX, PubMed XML/JSON, Semantic Scholar JSON/PDF links, Crossref JSON/XML) through OpenZiti/Pomerium for mTLS, logging, rate-limiting, and content scanning/redaction (e.g., strip potential trackers).
Secretless Architecture: Dynamic cred fetch from local CA; no env vars.
Immutable Logs & Tamper Detection: Append-only JSONL with hash chaining for every fetch/ingest; nightly integrity verification.
User-Friendly Feedback: Post-query/ingestion send encrypted summary + posture score + format coverage stats (e.g., "Paper ingested with PDF, HTML, TeX source; 97% secure — no anomalies") via webhook.
API Surface:

POST /research/search: { query: string, apis?: ["arxiv", "pubmed", "semanticscholar", "crossref"], max_results?: 10, date_from?: YYYY-MM-DD, date_to?: YYYY-MM-DD } → returns list of papers with available formats metadata (no full content).
POST /research/ingest: { paper_id: string or url: string, source_api?: string, formats?: ["pdf", "html", "tex", "json", "xml", "all"] } → fetches all requested/available formats, validates, stores hashes, chunks primary (PDF/HTML preferred), embeds.
POST /research/query: { question: string, format_preference?: ["pdf", "html", "tex", ...] } → RAG retrieval prioritizing chunks from preferred formats if available.
Step 1 — Search:

Decompose query into API-specific terms (e.g., ArXiv: categories like cs.AI; PubMed: MeSH terms).
Call APIs through ZT gateway; collect metadata including all links to export formats.
Return structured results with format availability per paper (e.g., { "pdf": true, "html": true, "tex_source": true, "json_metadata": true }).
Step 2 — Ingestion (Multi-Format Extraction):

For each selected paper, attempt to fetch all available formats via gateway (respect robots.txt, rate limits).
Supported formats per source (hard-coded + dynamic detection):
arXiv:
PDF: always (arxiv.org/pdf/{id}.pdf)
HTML: for papers submitted after ~Dec 2023 with successful conversion (arxiv.org/html/{id})
TeX/LaTeX source: .tar.gz or .zip source bundle (arxiv.org/src/{id}) — extract .tex, .bib, figures
JSON/XML metadata: from API response (Atom XML or convert to JSON)
No native PS/PostScript (deprecated)
Semantic Scholar:
PDF: openAccessPdf.url if available (public PDFs only)
JSON metadata: full paper details via API (title, authors, abstract, citations, embeddings)
No native HTML/TeX (fallback to PDF parsing if needed)
PubMed / NCBI E-utils:
XML: default/full record (eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed)
JSON: supported via retmode=json
MEDLINE/text: rettype=medline
Abstract/summary: rettype=abstract/docsum
No direct PDF/HTML/TeX (link to publisher if present)
Crossref:
JSON: default via REST API (api.crossref.org/works/{doi})
XML: via content negotiation (Accept: application/xml) or XML API
RDF/XML, schema.org/JSON-LD, etc. via content negotiation
PDF/full-text: link array if TDM/open-access (not always direct)
No native TeX/HTML/PS
For each format fetched:
Compute SHA-256 hash → store in formats table (UNIQUE constraint on paper_id + format_type + hash).
Store file locally (e.g., papers/{paper_id}/{format_type}.{ext}) or keep URL if ephemeral.
Validate: size >0, no error pages, hash matches if source provides.
Primary chunking/embedding: Prefer HTML (structured/clean) > PDF (via pdfplumber/GROBID) > TeX (parse .tex) > JSON/XML (extract abstract/sections).
Deduplication: Skip format if hash already exists for that paper/format_type.
Quality Validation: Reject bad fetches; log via immutable chain.
Step 3 — Retrieval (RAG):

Embed query; cosine search chunks (sqlite-vec or in-memory).
Prioritize chunks from user-preferred formats if available.
Top 10 deduped by paper; LLM synthesize: "Answer using context; cite paper + format used."
Observability: Track format usage stats, low-relevance flags.
Step 4 — Analysis (Optional):

LLM for cross-format summaries/trends (e.g., compare TeX equations vs PDF render).
Lineage: Sign outputs with provenance (which formats contributed).
Concurrency: Lockfile with posture check.

Deliverables:

CLI: search "<query>", ingest "<id-or-url>" --formats=all, query "<question>".
HTTP endpoints as above.
README: Setup APIs, multi-format details per source, ZT integrations (e.g., step-ca config), breach sim scripts.
Tests: Synthetic searches, format-specific fetches (mock responses), ZT failures (e.g., bad posture blocks).
Inspire from academic search tools; ensure ZT automation via crons/hooks (rotations, sims, audits). Integrate with #2 KB for cross-tool RAG if configured. Prioritize reliable formats (PDF + HTML + metadata) as fallback chain.

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40 sources
create a uniform cross-use case memory framework

14) Uniform Cross-Use Case Memory Framework — Implementation Prompt
You are a backend/infra engineer.
Implement a uniform cross-use case memory framework that provides shared, persistent storage and retrieval for data/memories across all OpenClaw use cases (#1–13), enabling seamless data sharing (e.g., CRM contacts in RAG queries, council signals from YouTube analytics, scientific paper chunks in content ideas). Use a hybrid key-value + vector store for structured data, embeddings, and provenance. Support store/retrieve/update/forget operations with semantic search. Further expanded with machine identities for memory agents, policy-as-code, post-quantum agility, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless arch, immutable logs, user feedback, and open-source tools (OpenZiti, OPA, step-ca).

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite cross_memory.db with WAL mode, foreign keys, and sqlite-vec for vectors (fallback to in-memory cosine).
Tables:
memories: id (PK UUID), use_case_id (references source use case #1–13), key (TEXT UNIQUE per use_case, e.g., "contact:email@domain"), value (JSON/BLOB for data), type (e.g., "contact", "chunk", "signal", "idea", "metric", "paper"), tags (JSON array), created_at, updated_at, expiry_at (for auto-forget).
embeddings: id (PK), memory_id (FK), embedding (BLOB), embedding_dim, provider (e.g., "gemini"), model, updated_at.
provenance: id (PK), memory_id (FK), source (JSON: {use_case: int, operation: string, timestamp: datetime}), merkle_hash (TEXT for chain), signature (BLOB post-quantum).
access_logs: timestamp, memory_id, action (store/retrieve/update/forget), actor (machine identity), policy_decision (JSON from OPA).
Indexes: On memories.use_case_id + key, embeddings.memory_id, provenance.memory_id.
Cache: In-memory LRU (size 5000) for frequent accesses, keyed by use_case + key.
Further Zero Trust Additions (Automated):

Machine Identity: Certs (via step-ca) for memory access agents in each use case; verify before store/retrieve. Automate rotation every 15min.
Policy-as-Code: OPA/Cerbos evaluates all ops (e.g., "allow retrieve if use_case requester matches owner AND data not expired"); policies define cross-sharing rules (e.g., CRM PII only to #10 tasks). Central repo with auto-drift detection.
Post-Quantum Readiness: Hybrid crypto (OpenQuantumSafe) for signatures, hashes, and encrypted values (e.g., sensitive memories).
Device/Posture Checks: osquery verifies host pre-op; block if failed.
Data Lineage & Provenance: Every memory signed with Merkle chain linking to source use case/operation; API auto-generates/verifies on access.
Automated Breach Simulation: Monthly inject tampered memories (altered values/hashes); detect via provenance checks and auto-forget/quarantine.
Observability & AI Monitoring: Prometheus metrics on access patterns, cross-use sharing frequency, embedding drifts (consistency checks); dashboard for posture scores and memory health (e.g., expiry alerts).
Central ZT Gateway: Route all cross-use interactions (e.g., #2 RAG querying #1 CRM embeds) through OpenZiti/Pomerium for mTLS, logging, and policy enforcement.
Secretless Architecture: Dynamic cred fetch from local CA; no env vars.
Immutable Logs & Tamper Detection: Append-only JSONL for access_logs with hash chaining; nightly integrity verification.
User-Friendly Feedback: Post-op send encrypted summary + posture score + stats (e.g., "Memory stored; shared with 2 use cases; 99% secure — expiry in 30 days") via webhook.
API Surface:

POST /memory/store: { use_case_id: int, key: string, value: JSON/BLOB, type: string, tags?: array, expiry_days?: int, embed?: bool (auto-embed if true) } → stores, embeds if requested, signs provenance; returns memory_id.
GET /memory/retrieve: { use_case_id?: int, key?: string, query?: string (semantic search if no key) } → policy-checked fetch; supports filters (type, tags); returns value + provenance.
PATCH /memory/update: { memory_id: UUID, updates: JSON (partial value/tags/expiry) } → updates with new provenance entry.
DELETE /memory/forget: { memory_id: UUID or key: string } → soft-delete (mark expired) or hard-purge per policy.
POST /memory/search: { query: string, use_case_ids?: array, types?: array, min_score?: float } → semantic vector search across embeddings; returns ranked memories with scores + provenance.
Integration with Use Cases (#1–13):

Hook into existing tools: e.g., #1 CRM stores contacts as memories (key: "contact:email"); #2 RAG chunks as "chunk:source_id"; #13 papers/formats as "paper:arxiv_id" with sub-keys for formats.
Cross-Sharing: e.g., #6 council retrieves signals from #5 YouTube metrics via /retrieve; #3 ideas searches #13 papers via /search.
Auto-Embed: On store, if embed=true and value text-based, generate embedding (Gemini/OpenAI via agent identity).
Expiry/Auto-Forget: Cron scans expiry_at; policy-based purge (e.g., PII after 60 days).
Multi-Format Support (from #13): When ingesting from #13, store each format as sub-memories (key: "paper:arxiv_id:format_pdf") with shared provenance.
Step 1 — Store Operation:

Validate input via OPA (e.g., "allow if type permitted for use_case").
Generate UUID id; store value (encrypt if sensitive per policy).
If embed: Chunk text (800 chars, 200 overlap), embed batches, store in embeddings.
Sign provenance: Include source use_case, operation details, Merkle hash linking to prior chain.
Cache entry; log immutably.
Step 2 — Retrieve/Search Operation:

Check posture/device; verify caller identity.
OPA evaluate (e.g., cross-use rules).
For key-based: Direct SQL fetch + decrypt.
For semantic: Embed query, cosine over embeddings (sqlite-vec); filter by use_case/types.
Return with provenance verification (fail if tampered).
Observability: Log access patterns; flag anomalies (e.g., frequent sensitive retrieves).
Step 3 — Update/Forget:

Similar auth/policy as store; append new provenance entry for updates.
Forget: Set expiry_at to now or purge if policy allows; cascade to embeddings.
Concurrency: Row-level locks; distributed if scaled beyond single SQLite.

Deliverables:

CLI: memory-store --use-case 1 --key "contact:example" --value '{"name":"Hal"}' --embed, memory-search "quantum papers".
HTTP endpoints as above.
README: Integration examples (e.g., hook #1 ingest to /store), ZT setup, policy samples (Rego for cross-sharing), breach sim scripts.
Tests: Cross-use scenarios (e.g., store in #1, retrieve in #10), ZT failures (bad identity blocks).
Inspire from vector DBs like Pinecone but local/SQLite-focused; ensure uniform API abstracts per-use-case storage (migrate existing DBs via cron). ZT automation via crons/hooks (rotations, sims, audits, expiry).

1.6s
Make schema more modular

Here is the updated #14) Uniform Cross-Use Case Memory Framework — Implementation Prompt with a more modular schema design.

The redesign draws from SQLite best practices (explicit types, versioned migrations, normalization with controlled denormalization for performance, separate concerns via tables/views/extensions), vector DB patterns (dedicated virtual tables or prefixed tables for embeddings), and modular/local-first patterns (per-use-case namespaces, pluggable type handlers, provenance as separate audit trail, access control via views/policies).

Key modularity improvements:

Namespace isolation via namespace column (maps to use_case_id but allows sub-namespaces, e.g., "crm:contact", "kb:source:arxiv").
Pluggable memory types with a memory_types registry table (extensible without schema changes for new use cases).
Separated concerns: Core data, embeddings (virtual or prefixed), provenance (immutable audit), access control (views + OPA integration).
Versioned schema support built-in (schema_version table + migration hooks).
Extensibility for new formats/sub-entities without altering core tables (via JSON extensions or child tables).
Performance-friendly for reads (indexes, materialized views where possible).
ZT alignment preserved and enhanced (provenance now links to access_logs for full traceability).
14) Uniform Cross-Use Case Memory Framework — Implementation Prompt (Modular Schema Update)
You are a backend/infra engineer.
Implement a uniform cross-use case memory framework that provides shared, persistent storage and retrieval for data/memories across all OpenClaw use cases (#1–13), enabling seamless data sharing (e.g., CRM contacts in RAG queries, council signals from YouTube analytics, scientific paper chunks in content ideas). Use a highly modular hybrid key-value + vector store for structured data, embeddings, and provenance. Support store/retrieve/update/forget operations with semantic search. Further expanded with machine identities for memory agents, policy-as-code, post-quantum agility, device checks, data lineage, breach simulation, observability, central ZT gateway, secretless arch, immutable logs, user feedback, and open-source tools (OpenZiti, OPA, step-ca).

Tech & storage:

Language: Python 3.11 (FastAPI + SQLAlchemy) or Node 20 + TypeScript (Express + Prisma).
Database: SQLite cross_memory.db with WAL mode, foreign keys, sqlite-vec (or fallback in-memory cosine).
Schema Versioning: Include schema_version table (id PK, version INTEGER, applied_at DATETIME, migration_notes TEXT); enforce migrations on startup (e.g., Alembic-lite or custom script).
Modular Core Tables (separated concerns for extensibility/maintainability):

namespaces (use-case / module isolation — extensible without altering core)
id INTEGER PRIMARY KEY
name TEXT UNIQUE NOT NULL (e.g., "crm", "kb", "arxiv", "council", "sub:contact")
description TEXT
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
owner_use_case INTEGER (references original #1–13 if applicable)
memory_types (pluggable types — new use cases register without schema change)
id INTEGER PRIMARY KEY
namespace_id INTEGER FK → namespaces.id
type_name TEXT NOT NULL (e.g., "contact", "chunk", "signal", "paper_format", "metric")
handler_module TEXT (optional: Python/JS module for custom serialization/deserialization)
embed_by_default BOOLEAN DEFAULT FALSE
expires_default_days INTEGER DEFAULT NULL
UNIQUE (namespace_id, type_name)
memories (core entity — normalized, namespace-scoped)
id UUID PRIMARY KEY (use uuid_generate_v4() or equivalent)
namespace_id INTEGER NOT NULL FK → namespaces.id
memory_type_id INTEGER NOT NULL FK → memory_types.id
key TEXT NOT NULL (unique within namespace, e.g., "email:john@acme.com", "paper:2401.12345:pdf")
value BLOB NOT NULL (JSON-serialized or raw binary; encrypt sensitive via policy)
tags JSON (array of strings)
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
updated_at DATETIME DEFAULT CURRENT_TIMESTAMP
expiry_at DATETIME DEFAULT NULL
schema_version INTEGER NOT NULL (references current schema_version)
UNIQUE (namespace_id, key)
INDEX idx_memories_namespace_key (namespace_id, key)
INDEX idx_memories_type (memory_type_id)
INDEX idx_memories_expiry (expiry_at) WHERE expiry_at IS NOT NULL
embeddings (modular vector storage — can use sqlite-vec virtual table per namespace or prefixed)
For maximum modularity: Create virtual tables dynamically per namespace (e.g., vec_memories_{namespace_id}) via sqlite-vec
Example schema (created on namespace registration):
CREATE VIRTUAL TABLE vec_memories_1 USING vec0(
rowid INTEGER PRIMARY KEY,
memory_id UUID,
embedding BLOB
);
Fallback (if no sqlite-vec):
id INTEGER PRIMARY KEY
memory_id UUID FK → memories.id
embedding BLOB
embedding_dim INTEGER
provider TEXT
model TEXT
updated_at DATETIME
INDEX idx_embeddings_memory (memory_id)
provenance (immutable audit trail — append-only)
id INTEGER PRIMARY KEY AUTOINCREMENT
memory_id UUID FK → memories.id
operation TEXT NOT NULL ("store", "update", "retrieve", "forget")
actor_identity TEXT (machine cert fingerprint)
source_context JSON (e.g., {use_case: 1, operation: "ingest", timestamp: ...})
merkle_hash TEXT NOT NULL (hash of value + previous provenance id)
signature BLOB (post-quantum signature)
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
INDEX idx_provenance_memory (memory_id)
access_logs (for observability & ZT auditing — append-only)
id INTEGER PRIMARY KEY AUTOINCREMENT
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
memory_id UUID FK → memories.id
action TEXT NOT NULL
actor TEXT (identity)
policy_decision JSON (OPA output)
outcome TEXT ("allowed", "denied")
client_info JSON (device posture hash, IP if relevant)
Modularity Features & Extensibility:

Namespace registration: On first use of a use case (#1–13), auto-create namespace + default memory_types (e.g., for #13: "paper", "paper_format:pdf", "paper_format:tex").
Dynamic virtual tables for embeddings: When registering namespace with embed_by_default=true, create vec_ table automatically.
Custom handlers: memory_types.handler_module allows per-type serialization (e.g., #13 multi-format → store PDF/TeX as sub-keys or child records).
Views for access control: e.g., CREATE VIEW accessible_memories AS SELECT * FROM memories WHERE OPA_allow(...); (integrate with policy engine).
Migrations: Startup script checks schema_version; applies additive changes (ADD COLUMN, new tables) without data loss.
Sub-entities: For complex types (e.g., #13 formats), use key suffixes ("paper:arxiv_id:format_pdf") or optional child table memory_children (parent_id FK → memories.id).
Further Zero Trust Additions (Automated):
(Unchanged from previous version, but now tighter integration with modular tables)

OPA policies reference namespace_id + memory_type_id for fine-grained rules (e.g., "deny cross-namespace read of crm PII").
Provenance chains link across namespaces for full cross-use traceability.
Access_logs capture namespace/type for anomaly detection (e.g., unusual cross-use spikes).
API Surface: (unchanged, but now namespace-aware)

POST /memory/store: { namespace: string (or use_case_id), key: string, value: any, type: string, tags?: array, expiry_days?: int, embed?: bool }
GET /memory/retrieve: { namespace?: string, key?: string, query?: string, types?: array }
PATCH /memory/update, DELETE /memory/forget: Support namespace + key or id.
POST /memory/search: { query: string, namespaces?: array, types?: array, min_score?: float }
Integration with Use Cases (#1–13):

Each use case registers/uses its namespace (e.g., #1 → "crm", #13 → "arxiv").
Cross-Sharing: e.g., #6 council searches "signals" from #5 via semantic query filtered to namespace "youtube".
Auto-Embed: Per-type config; chunk/embed value (prefer text fields in JSON).
Step 1 — Store Operation:

Resolve/create namespace & type.
OPA check.
Insert into memories → if embed, insert into corresponding vec_ table.
Append provenance + access_log.
Cache + feedback.
Step 2 — Retrieve/Search:

Posture + identity check.
OPA evaluate.
Semantic: Query appropriate vec_ tables (union if multi-namespace).
Return with verified provenance.
Concurrency: Use transactions; row locks for updates.

Deliverables:

CLI/HTTP as before, plus memory-register-namespace --name crm --types contact,thread.
README: Migration guide, namespace/type examples, dynamic vec table creation code, policy samples for cross-namespace sharing.
Tests: Multi-namespace search, type registration, provenance verification, ZT denials.
Inspire from modular SQLite patterns (VFS/extensions, virtual tables), vector DB best practices (separate vector tables), and local-first designs (namespace isolation). Ensures schema evolves additively while keeping concerns separated and use-case extensions trivial. ZT automation via crons/hooks unchanged.

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