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RESEARCH & INTELLIGENCE WORKFLOWS

Complete workflow definitions for research and intelligence operations across 5 specialized categories.

Version: 1.0.0 Generated: 2025-12-12 Total Workflows: 50 (10 per category)

Web Research​

1. competitive-landscape-discovery​

  • Description: Identify and profile all major competitors in a market segment with multi-source validation and tier categorization
  • Trigger: /research --type competitive-landscape "[market segment]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: competitive-market-analyst, web-search-researcher, business-intelligence-analyst
    • Commands: /smart-research, /research, /multi-agent-research
  • Steps:
    1. Market scoping - competitive-market-analyst - Define market boundaries and segment focus
    2. Competitor discovery - web-search-researcher - Identify all major and emerging players using multi-source research
    3. Tier categorization - competitive-market-analyst - Categorize competitors into Tier 1/2/3 based on market share and impact
    4. Profile creation - business-intelligence-analyst - Create comprehensive profiles for top 10-15 competitors
    5. Market dynamics - competitive-market-analyst - Analyze competitive intensity, barriers to entry, market structure
    6. Validation - QA agent - Cross-reference findings across 3+ authoritative sources
    7. Report generation - documentation-writer - Synthesize findings into actionable intelligence report
  • Tags: competitive-intelligence, market-research, discovery, validation

2. pricing-intelligence-gathering​

  • Description: Research and analyze competitor pricing strategies, tiers, and value propositions across market
  • Trigger: /research --type pricing "[company names or market]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: competitive-market-analyst, web-search-researcher
    • Commands: /smart-research, /research
  • Steps:
    1. Competitor identification - competitive-market-analyst - Identify pricing research targets
    2. Official pricing - web-search-researcher - Gather official pricing from company websites and documentation
    3. Tier analysis - competitive-market-analyst - Map pricing tiers, feature breakdown, and target segments
    4. Value proposition - business-intelligence-analyst - Analyze pricing-to-value ratio and positioning
    5. Market positioning - competitive-market-analyst - Compare pricing strategies and identify market gaps
    6. Validation - QA agent - Verify pricing accuracy through multiple official sources
    7. Matrix creation - documentation-writer - Create pricing comparison matrix with recommendations
  • Tags: pricing-strategy, competitive-intelligence, market-analysis, validation

3. technology-trend-analysis​

  • Description: Research emerging technology trends, adoption patterns, and future market directions with expert analysis
  • Trigger: /research --type trends "[technology area]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: web-search-researcher, research-agent, business-intelligence-analyst
    • Commands: /smart-research, /research
  • Steps:
    1. Trend scoping - research-agent - Define technology domain and timeframe for analysis
    2. Source identification - web-search-researcher - Find analyst reports, industry publications, expert opinions
    3. Pattern recognition - research-agent - Identify emerging patterns, adoption curves, technology maturity
    4. Driver analysis - business-intelligence-analyst - Analyze key drivers, barriers, and market forces
    5. Impact assessment - competitive-market-analyst - Evaluate strategic implications and competitive impact
    6. Timeline projection - research-agent - Create adoption timeline and maturity projections
    7. Strategic synthesis - business-intelligence-analyst - Generate strategic recommendations for technology positioning
  • Tags: trend-analysis, technology-research, strategic-intelligence, forecasting

4. market-sizing-research​

  • Description: Calculate TAM/SAM/SOM using bottom-up and top-down methodologies with multi-source validation
  • Trigger: /research --type market-size "[market]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: business-intelligence-analyst, web-search-researcher, research-agent
    • Commands: /smart-research, /research
  • Steps:
    1. Market definition - business-intelligence-analyst - Define market boundaries, segments, and scope
    2. TAM research - web-search-researcher - Top-down analysis using industry reports and analyst data
    3. SAM calculation - business-intelligence-analyst - Bottom-up segmentation and addressable market calculation
    4. SOM projection - business-intelligence-analyst - Realistic market capture modeling over 3-5 years
    5. Growth analysis - research-agent - Historical trends and future growth projections with CAGR
    6. Cross-validation - QA agent - Validate estimates across multiple methodologies for <20% variance
    7. Report generation - documentation-writer - Create comprehensive market sizing report with confidence intervals
  • Tags: market-sizing, TAM-SAM-SOM, business-intelligence, validation, financial-modeling

5. osint-investigation​

  • Description: Open-source intelligence gathering for competitive intelligence, due diligence, and strategic research
  • Trigger: /research --type osint "[target company/topic]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: web-search-researcher, research-agent, competitive-market-analyst
    • Commands: /smart-research, /research, /web-search
  • Steps:
    1. Scope definition - research-agent - Define investigation parameters and information requirements
    2. Public records - web-search-researcher - Search company filings, press releases, official statements
    3. Social signals - web-search-researcher - Analyze social media, community presence, developer relations
    4. Partnership tracking - competitive-market-analyst - Identify partnerships, integrations, ecosystem relationships
    5. Funding intelligence - business-intelligence-analyst - Track funding rounds, valuations, investor activity
    6. Sentiment analysis - research-agent - Analyze user feedback, reviews, community sentiment
    7. Intelligence synthesis - competitive-market-analyst - Synthesize findings into actionable intelligence report
  • Tags: OSINT, competitive-intelligence, due-diligence, investigation, multi-source

6. feature-comparison-analysis​

  • Description: Systematic feature comparison across competitors with scoring and gap identification
  • Trigger: /research --type features "[competitors list]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: competitive-market-analyst, web-search-researcher
    • Commands: /smart-research, /research
  • Steps:
    1. Feature taxonomy - competitive-market-analyst - Define feature categories and evaluation criteria
    2. Feature discovery - web-search-researcher - Catalog features for each competitor from official sources
    3. Capability assessment - competitive-market-analyst - Rate capabilities as Excellent/Good/Average/Poor/N/A
    4. Matrix construction - documentation-writer - Create side-by-side comparison matrix
    5. Gap analysis - competitive-market-analyst - Identify market gaps and differentiation opportunities
    6. Scoring - business-intelligence-analyst - Calculate weighted scores by feature category
    7. Recommendations - competitive-market-analyst - Generate strategic positioning recommendations
  • Tags: feature-comparison, competitive-analysis, gap-analysis, product-intelligence

7. regulatory-compliance-research​

  • Description: Research regulatory requirements, compliance standards, and legal considerations for market entry
  • Trigger: /research --type compliance "[regulation/region]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: web-search-researcher, research-agent, security-specialist
    • Commands: /research, /smart-research
  • Steps:
    1. Scope definition - research-agent - Define regulatory domain, geography, and compliance requirements
    2. Standards research - web-search-researcher - Identify applicable regulations (GDPR, SOC2, ISO, HIPAA, etc.)
    3. Requirement mapping - security-specialist - Map regulatory requirements to technical/operational controls
    4. Industry benchmarks - competitive-market-analyst - Research competitor compliance certifications
    5. Cost analysis - business-intelligence-analyst - Estimate compliance costs and timeline
    6. Risk assessment - security-specialist - Identify compliance gaps and risk exposure
    7. Roadmap creation - project-manager - Create compliance roadmap with milestones and deliverables
  • Tags: compliance-research, regulatory-intelligence, security, risk-assessment, legal

8. customer-sentiment-analysis​

  • Description: Analyze customer reviews, feedback, and sentiment for competitors to identify strengths/weaknesses
  • Trigger: /research --type sentiment "[competitor/product]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: web-search-researcher, research-agent, competitive-market-analyst
    • Commands: /smart-research, /research
  • Steps:
    1. Source identification - web-search-researcher - Find review sites, forums, social media, G2, Capterra
    2. Review collection - web-search-researcher - Gather customer feedback from multiple sources
    3. Sentiment scoring - research-agent - Categorize feedback as positive/neutral/negative
    4. Theme extraction - research-agent - Identify common themes, pain points, and praise patterns
    5. Strength/weakness mapping - competitive-market-analyst - Map to competitor strengths and vulnerabilities
    6. Opportunity identification - competitive-market-analyst - Identify unmet needs and market opportunities
    7. Report generation - documentation-writer - Create sentiment analysis report with actionable insights
  • Tags: sentiment-analysis, customer-research, competitive-intelligence, voice-of-customer

9. investment-funding-tracking​

  • Description: Track funding rounds, valuations, and investment activity in market segment for strategic intelligence
  • Trigger: /research --type funding "[market/companies]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: web-search-researcher, business-intelligence-analyst
    • Commands: /smart-research, /research
  • Steps:
    1. Market scoping - business-intelligence-analyst - Define market segment and time period for analysis
    2. Funding data - web-search-researcher - Search Crunchbase, PitchBook, press releases for funding announcements
    3. Valuation tracking - business-intelligence-analyst - Track valuations and funding stage progression
    4. Investor mapping - research-agent - Identify active investors, investment patterns, sector focus
    5. Trend analysis - business-intelligence-analyst - Analyze funding trends, market heat, investment velocity
    6. Competitive positioning - competitive-market-analyst - Compare funding levels and market traction
    7. Strategic insights - business-intelligence-analyst - Generate insights for fundraising and positioning
  • Tags: funding-intelligence, investment-tracking, market-intelligence, valuation-analysis

10. partnership-ecosystem-mapping​

  • Description: Map partnership networks, integrations, and ecosystem relationships for competitive advantage
  • Trigger: /research --type partnerships "[company/market]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: web-search-researcher, competitive-market-analyst
    • Commands: /smart-research, /research
  • Steps:
    1. Target identification - competitive-market-analyst - Identify companies and ecosystem focus
    2. Partnership discovery - web-search-researcher - Find announced partnerships, integrations, reseller agreements
    3. Integration mapping - research-agent - Map technical integrations and API partnerships
    4. Ecosystem analysis - competitive-market-analyst - Analyze ecosystem strategy and positioning
    5. Network effects - business-intelligence-analyst - Evaluate network effects and competitive moats
    6. Gap identification - competitive-market-analyst - Identify partnership opportunities and gaps
    7. Strategy recommendations - competitive-market-analyst - Generate partnership strategy recommendations
  • Tags: partnership-intelligence, ecosystem-analysis, competitive-strategy, integration-research

Data Analytics​

11. data-collection-pipeline-setup​

  • Description: Design and implement automated data collection pipelines for research and analytics workflows
  • Trigger: /setup-pipeline --source "[data source]" --destination "[storage]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineering, backend-architect, devops-engineer
    • Commands: /build-project, /test
  • Steps:
    1. Requirements gathering - data-engineering - Define data sources, frequency, volume, and quality requirements
    2. Architecture design - backend-architect - Design pipeline architecture with extraction, transformation, loading
    3. Source integration - data-engineering - Implement data source connectors (APIs, databases, files, streams)
    4. Transformation logic - data-engineering - Build data cleaning, normalization, enrichment transformations
    5. Storage setup - backend-architect - Configure data storage (data lake, warehouse, database)
    6. Scheduling - devops-engineer - Implement scheduling and orchestration (Airflow, Prefect, cron)
    7. Monitoring - devops-engineer - Add data quality checks, error handling, alerting
    8. Testing - QA-automation - Test pipeline end-to-end with production-like data volumes
  • Tags: data-pipeline, ETL, data-engineering, automation, infrastructure

12. research-data-visualization​

  • Description: Create interactive visualizations and dashboards for research findings and market intelligence
  • Trigger: /visualize-data --source "[data file]" --type "[chart types]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: data-engineering, frontend-developer
    • Commands: /build-project
  • Steps:
    1. Data analysis - data-engineering - Analyze data structure, dimensions, metrics for visualization
    2. Visualization design - frontend-developer - Design dashboard layout, chart types, interaction patterns
    3. Tool selection - data-engineering - Choose visualization tools (Plotly, D3.js, Tableau, Grafana)
    4. Data preparation - data-engineering - Transform data into visualization-ready format
    5. Chart implementation - frontend-developer - Build interactive charts with filtering, drill-down
    6. Dashboard assembly - frontend-developer - Assemble charts into cohesive dashboard with navigation
    7. Export/share - frontend-developer - Configure export options (PDF, PNG, interactive HTML)
    8. Testing - QA-automation - Test interactivity, responsiveness, data accuracy
  • Tags: data-visualization, dashboards, analytics, business-intelligence, interactive

13. competitive-metrics-tracking​

  • Description: Automated tracking and trending of competitor metrics (pricing, features, performance, market share)
  • Trigger: /track-metrics --competitors "[list]" --frequency "[schedule]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: data-engineering, competitive-market-analyst, web-search-researcher
    • Commands: /setup-pipeline, /smart-research
  • Steps:
    1. Metric definition - competitive-market-analyst - Define KPIs to track (pricing, features, reviews, traffic)
    2. Data source setup - web-search-researcher - Identify data sources (websites, APIs, public databases)
    3. Collection automation - data-engineering - Build automated scrapers and API integrations
    4. Storage schema - data-engineering - Design time-series database schema for metric history
    5. Change detection - data-engineering - Implement change detection and alerting for significant shifts
    6. Trend analysis - business-intelligence-analyst - Calculate trends, growth rates, competitive positioning
    7. Dashboard creation - frontend-developer - Build real-time competitive intelligence dashboard
    8. Alerting - devops-engineer - Configure alerts for competitive changes (pricing drops, new features)
  • Tags: competitive-tracking, metrics-automation, time-series, analytics, monitoring

14. market-data-aggregation​

  • Description: Aggregate market data from multiple sources into unified research database for analysis
  • Trigger: /aggregate-data --sources "[source list]" --market "[segment]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineering, research-agent, backend-architect
    • Commands: /setup-pipeline, /research
  • Steps:
    1. Source identification - research-agent - Identify authoritative data sources (Gartner, IDC, govt stats)
    2. Schema design - backend-architect - Design unified schema for multi-source data integration
    3. Extraction logic - data-engineering - Build extractors for each data source format
    4. Deduplication - data-engineering - Implement entity resolution and duplicate detection
    5. Normalization - data-engineering - Normalize metrics, currencies, time periods across sources
    6. Quality validation - data-engineering - Validate data quality, completeness, consistency
    7. Storage optimization - backend-architect - Optimize storage for analytical queries
    8. API creation - backend-architect - Build query API for research access to aggregated data
  • Tags: data-aggregation, data-integration, ETL, research-database, normalization

15. sentiment-trend-analysis​

  • Description: Track customer sentiment trends over time using natural language processing and sentiment scoring
  • Trigger: /analyze-sentiment --source "[reviews/social]" --timeframe "[period]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: data-engineering, research-agent
    • Commands: /research, /visualize-data
  • Steps:
    1. Data collection - research-agent - Gather reviews, social mentions, feedback over time period
    2. Text preprocessing - data-engineering - Clean, tokenize, normalize text data
    3. Sentiment scoring - data-engineering - Apply sentiment analysis (VADER, TextBlob, or ML model)
    4. Topic extraction - research-agent - Extract topics and themes using NLP (LDA, keywords)
    5. Time-series aggregation - data-engineering - Aggregate sentiment scores by day/week/month
    6. Trend calculation - data-engineering - Calculate moving averages, trend lines, change points
    7. Visualization - frontend-developer - Create sentiment trend charts with topic breakdown
    8. Insight generation - research-agent - Identify significant shifts and underlying causes
  • Tags: sentiment-analysis, NLP, trend-analysis, customer-intelligence, time-series

16. roi-calculator-development​

  • Description: Build ROI calculators for product value propositions and customer business case analysis
  • Trigger: /build-roi-calculator --model "[pricing model]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: business-intelligence-analyst, frontend-developer, backend-architect
    • Commands: /build-project
  • Steps:
    1. Model design - business-intelligence-analyst - Define ROI calculation methodology and variables
    2. Input parameters - business-intelligence-analyst - Identify customer inputs (team size, productivity, cost)
    3. Calculation logic - backend-architect - Implement ROI calculation with scenario modeling
    4. UI design - frontend-developer - Design intuitive calculator interface with real-time updates
    5. Visualization - frontend-developer - Add charts showing ROI over time, payback period, NPV
    6. Validation - business-intelligence-analyst - Validate calculations against customer case studies
    7. Documentation - documentation-writer - Create methodology documentation and assumptions
    8. Testing - QA-automation - Test edge cases, rounding, formula accuracy
  • Tags: ROI-calculator, business-intelligence, value-proposition, sales-enablement

17. research-report-automation​

  • Description: Automate generation of research reports from data pipelines with templating and scheduling
  • Trigger: /automate-report --template "[template]" --schedule "[frequency]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: data-engineering, documentation-writer, backend-architect
    • Commands: /build-project, /setup-pipeline
  • Steps:
    1. Template design - documentation-writer - Create report templates with placeholders for data
    2. Data pipeline - data-engineering - Build data collection and aggregation pipeline
    3. Report generation - backend-architect - Implement template rendering engine (Jinja2, Handlebars)
    4. Chart generation - data-engineering - Auto-generate charts and visualizations from data
    5. Quality checks - data-engineering - Validate data completeness before report generation
    6. Scheduling - devops-engineer - Schedule automated report generation (daily/weekly/monthly)
    7. Distribution - backend-architect - Implement email/Slack/API distribution of generated reports
    8. Version control - data-engineering - Archive generated reports with version tracking
  • Tags: report-automation, documentation, data-pipeline, scheduling, templating

18. competitor-pricing-tracker​

  • Description: Automated monitoring of competitor pricing changes with historical tracking and alerting
  • Trigger: /track-pricing --competitors "[list]" --alert-threshold "[%]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: data-engineering, competitive-market-analyst, web-search-researcher
    • Commands: /setup-pipeline, /track-metrics
  • Steps:
    1. Competitor setup - competitive-market-analyst - Define competitors and pricing pages to monitor
    2. Scraper development - data-engineering - Build web scrapers for pricing page extraction
    3. Price extraction - data-engineering - Implement price parsing with currency normalization
    4. Change detection - data-engineering - Compare current vs previous pricing, detect changes
    5. Historical storage - backend-architect - Store pricing history in time-series database
    6. Alert configuration - devops-engineer - Configure alerts for price changes exceeding threshold
    7. Reporting - frontend-developer - Create pricing change dashboard with trend analysis
    8. Validation - QA-automation - Validate scraper accuracy against manual checks
  • Tags: pricing-tracker, competitive-intelligence, automation, web-scraping, monitoring

19. analytics-dashboard-builder​

  • Description: Build customizable analytics dashboards for research KPIs and business metrics
  • Trigger: /build-dashboard --metrics "[KPI list]" --datasource "[source]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: frontend-developer, data-engineering, backend-architect
    • Commands: /build-project, /visualize-data
  • Steps:
    1. Requirements gathering - data-engineering - Define metrics, dimensions, user personas, refresh frequency
    2. Data modeling - backend-architect - Design data model optimized for dashboard queries
    3. Backend API - backend-architect - Build query API with filtering, aggregation, time-range support
    4. Framework selection - frontend-developer - Choose dashboard framework (React, Vue, Grafana, Metabase)
    5. Chart library - frontend-developer - Select chart library (Chart.js, Recharts, D3, Plotly)
    6. UI implementation - frontend-developer - Build responsive dashboard with filters, drill-downs
    7. Real-time updates - backend-architect - Implement WebSocket or polling for live data updates
    8. Export features - frontend-developer - Add PDF export, CSV download, screenshot capabilities
    9. Testing - QA-automation - Test performance, responsiveness, data accuracy across browsers
  • Tags: dashboards, analytics, business-intelligence, visualization, real-time

20. data-quality-monitoring​

  • Description: Implement data quality monitoring and validation for research pipelines and databases
  • Trigger: /monitor-quality --pipeline "[pipeline name]" --rules "[quality rules]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: data-engineering, QA-automation, devops-engineer
    • Commands: /setup-pipeline, /test
  • Steps:
    1. Rule definition - data-engineering - Define data quality rules (completeness, accuracy, consistency, timeliness)
    2. Schema validation - data-engineering - Implement schema validation and type checking
    3. Range checks - data-engineering - Add min/max, outlier detection, anomaly detection
    4. Deduplication - data-engineering - Detect and flag duplicate records
    5. Freshness monitoring - devops-engineer - Monitor data staleness and pipeline execution delays
    6. Alerting - devops-engineer - Configure alerts for quality threshold violations
    7. Dashboard - frontend-developer - Build data quality dashboard with trend tracking
    8. Remediation - data-engineering - Document remediation workflows for quality failures
  • Tags: data-quality, monitoring, validation, pipeline-reliability, anomaly-detection

Market Intelligence​

21. market-entry-analysis​

  • Description: Comprehensive analysis for market entry including barriers, opportunities, competitive positioning
  • Trigger: /analyze-market-entry --market "[segment]" --region "[geography]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: business-intelligence-analyst, competitive-market-analyst, research-agent
    • Commands: /smart-research, /research
  • Steps:
    1. Market definition - business-intelligence-analyst - Define market scope, geography, segment boundaries
    2. Market sizing - business-intelligence-analyst - Calculate TAM/SAM/SOM with growth projections
    3. Barrier analysis - competitive-market-analyst - Identify entry barriers (regulatory, capital, technology, brand)
    4. Competitive assessment - competitive-market-analyst - Map competitive landscape and intensity
    5. Customer research - research-agent - Analyze customer needs, pain points, buying behavior
    6. Risk assessment - business-intelligence-analyst - Evaluate market risks and mitigation strategies
    7. Financial modeling - business-intelligence-analyst - Project revenues, costs, profitability scenarios
    8. Strategy recommendations - competitive-market-analyst - Recommend entry strategy, positioning, GTM approach
  • Tags: market-entry, strategic-analysis, market-sizing, competitive-analysis, financial-modeling

22. competitive-positioning-strategy​

  • Description: Develop strategic positioning recommendations based on competitive analysis and market gaps
  • Trigger: /develop-positioning --product "[product]" --competitors "[list]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: competitive-market-analyst, business-intelligence-analyst, content-marketing
    • Commands: /smart-research, /research
  • Steps:
    1. Competitive profiling - competitive-market-analyst - Profile top competitors on features, pricing, positioning
    2. Feature gap analysis - competitive-market-analyst - Identify market gaps and underserved needs
    3. Customer segmentation - business-intelligence-analyst - Analyze target segments and personas
    4. Value proposition - competitive-market-analyst - Define unique value proposition and differentiation
    5. Messaging framework - content-marketing - Develop positioning statement and key messages
    6. Positioning matrix - competitive-market-analyst - Create 2x2 positioning matrix vs competitors
    7. Go-to-market - business-intelligence-analyst - Recommend GTM strategy aligned with positioning
    8. Validation - research-agent - Validate positioning with customer interviews and feedback
  • Tags: positioning-strategy, competitive-differentiation, value-proposition, messaging, GTM

23. industry-trend-monitoring​

  • Description: Continuous monitoring of industry trends, emerging technologies, and market shifts
  • Trigger: /monitor-trends --industry "[industry]" --frequency "[schedule]" or manual
  • Complexity: moderate
  • Duration: 15-30m (per scan)
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: web-search-researcher, research-agent, competitive-market-analyst
    • Commands: /smart-research, /setup-pipeline
  • Steps:
    1. Source configuration - research-agent - Define information sources (news, blogs, research, social)
    2. Keyword setup - web-search-researcher - Configure keyword monitoring and topic tracking
    3. Automated scanning - data-engineering - Build automated content aggregation pipeline
    4. Trend detection - research-agent - Apply NLP for topic modeling and trend emergence detection
    5. Impact analysis - competitive-market-analyst - Assess strategic impact of identified trends
    6. Alerting - devops-engineer - Configure alerts for high-impact trend signals
    7. Weekly digest - documentation-writer - Generate automated trend summary reports
    8. Archive - data-engineering - Maintain searchable archive of trend intelligence
  • Tags: trend-monitoring, market-intelligence, automation, industry-analysis, NLP

24. swot-analysis-execution​

  • Description: Comprehensive SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) for strategic planning
  • Trigger: /swot-analysis --company "[company]" --market "[segment]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: competitive-market-analyst, business-intelligence-analyst, research-agent
    • Commands: /smart-research, /research
  • Steps:
    1. Internal analysis - business-intelligence-analyst - Assess internal strengths and weaknesses (team, product, resources)
    2. External research - competitive-market-analyst - Research market opportunities and external threats
    3. Competitive benchmarking - competitive-market-analyst - Compare capabilities vs competitors
    4. Market dynamics - research-agent - Analyze market trends, customer needs, technology shifts
    5. SWOT synthesis - competitive-market-analyst - Populate SWOT matrix with validated findings
    6. Strategic implications - business-intelligence-analyst - Derive strategic actions from SWOT quadrants
    7. Prioritization - competitive-market-analyst - Prioritize actions by impact and urgency
    8. Documentation - documentation-writer - Create SWOT analysis report with recommendations
  • Tags: SWOT-analysis, strategic-planning, competitive-analysis, market-analysis, framework

25. market-share-estimation​

  • Description: Estimate market share for competitors using public data, proxies, and analytical models
  • Trigger: /estimate-share --market "[segment]" --competitors "[list]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: business-intelligence-analyst, research-agent, data-engineering
    • Commands: /smart-research, /research
  • Steps:
    1. Market sizing - business-intelligence-analyst - Calculate total market size (TAM) for normalization
    2. Data collection - research-agent - Gather public data (revenues, users, downloads, traffic)
    3. Proxy identification - business-intelligence-analyst - Identify proxy metrics (web traffic, app downloads, reviews)
    4. Estimation modeling - data-engineering - Build estimation models using regression, triangulation
    5. Cross-validation - research-agent - Validate estimates across multiple methodologies
    6. Confidence intervals - business-intelligence-analyst - Calculate confidence ranges for estimates
    7. Trend analysis - data-engineering - Track market share changes over time
    8. Reporting - documentation-writer - Create market share report with methodology transparency
  • Tags: market-share, estimation, competitive-intelligence, data-modeling, analytics

26. gtm-strategy-research​

  • Description: Research go-to-market strategies for product launches including channels, messaging, and tactics
  • Trigger: /research-gtm --product "[product]" --segment "[target]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: competitive-market-analyst, business-intelligence-analyst, content-marketing
    • Commands: /smart-research, /research
  • Steps:
    1. Market segmentation - business-intelligence-analyst - Define target segments and personas
    2. Competitor GTM - competitive-market-analyst - Research competitor GTM strategies and channels
    3. Channel analysis - business-intelligence-analyst - Evaluate channel effectiveness (direct, partners, PLG, sales)
    4. Messaging research - content-marketing - Analyze effective messaging and positioning in market
    5. Pricing strategy - business-intelligence-analyst - Research pricing models and customer acquisition economics
    6. Launch tactics - competitive-market-analyst - Identify successful launch tactics from comparables
    7. Timeline planning - project-manager - Create phased GTM timeline with milestones
    8. Success metrics - business-intelligence-analyst - Define KPIs and success criteria for GTM execution
  • Tags: GTM-strategy, go-to-market, product-launch, channel-strategy, market-research

27. acquisition-target-research​

  • Description: Research and evaluate potential acquisition targets for strategic M&A or partnership opportunities
  • Trigger: /research-acquisitions --criteria "[criteria]" --market "[segment]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: business-intelligence-analyst, research-agent, competitive-market-analyst
    • Commands: /smart-research, /research
  • Steps:
    1. Criteria definition - business-intelligence-analyst - Define acquisition criteria (strategic fit, size, technology, market)
    2. Target identification - research-agent - Identify potential targets using market maps and databases
    3. Financial analysis - business-intelligence-analyst - Analyze revenues, funding, burn rate, runway
    4. Strategic fit - competitive-market-analyst - Assess strategic value, synergies, market position
    5. Technology assessment - backend-architect - Evaluate technology stack, IP, team capabilities
    6. Risk analysis - business-intelligence-analyst - Identify integration risks, cultural fit, retention risks
    7. Valuation range - business-intelligence-analyst - Estimate valuation range using comparable transactions
    8. Prioritization - competitive-market-analyst - Rank targets by strategic value and acquisition feasibility
  • Tags: M&A-research, acquisition-targets, strategic-intelligence, valuation, due-diligence

28. customer-segment-analysis​

  • Description: Analyze customer segments for market prioritization, persona development, and targeting strategy
  • Trigger: /analyze-segments --market "[market]" --criteria "[segmentation]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: business-intelligence-analyst, research-agent, content-marketing
    • Commands: /smart-research, /research
  • Steps:
    1. Segmentation criteria - business-intelligence-analyst - Define segmentation dimensions (firmographics, behavior, needs)
    2. Data collection - research-agent - Gather data on customer characteristics, behaviors, preferences
    3. Cluster analysis - data-engineering - Apply clustering algorithms to identify natural segments
    4. Segment profiling - business-intelligence-analyst - Profile each segment by size, needs, willingness to pay
    5. Persona development - content-marketing - Create detailed personas for priority segments
    6. Opportunity sizing - business-intelligence-analyst - Calculate revenue opportunity by segment
    7. Prioritization - competitive-market-analyst - Prioritize segments by attractiveness and fit
    8. Targeting strategy - content-marketing - Develop segment-specific messaging and go-to-market tactics
  • Tags: segmentation-analysis, customer-research, personas, market-analysis, targeting

29. pricing-strategy-optimization​

  • Description: Optimize pricing strategy using competitive analysis, value-based pricing, and elasticity modeling
  • Trigger: /optimize-pricing --product "[product]" --market "[segment]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: business-intelligence-analyst, competitive-market-analyst, data-engineering
    • Commands: /smart-research, /research
  • Steps:
    1. Competitive pricing - competitive-market-analyst - Research competitor pricing tiers and strategies
    2. Value analysis - business-intelligence-analyst - Quantify customer value proposition and ROI
    3. Willingness to pay - research-agent - Conduct pricing research (Van Westendorp, conjoint analysis)
    4. Cost analysis - business-intelligence-analyst - Calculate unit economics and margin requirements
    5. Elasticity modeling - data-engineering - Model price elasticity and revenue optimization
    6. Tier design - business-intelligence-analyst - Design pricing tiers with feature differentiation
    7. Scenario analysis - business-intelligence-analyst - Model revenue scenarios across pricing options
    8. Recommendations - competitive-market-analyst - Recommend optimal pricing with implementation roadmap
  • Tags: pricing-optimization, pricing-strategy, value-based-pricing, competitive-pricing, revenue-optimization

30. brand-perception-research​

  • Description: Research brand perception, awareness, and positioning through surveys, social listening, and analysis
  • Trigger: /research-brand --brand "[brand]" --competitors "[list]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: web-search-researcher, research-agent, content-marketing
    • Commands: /smart-research, /research
  • Steps:
    1. Social listening - web-search-researcher - Monitor social media, forums, communities for brand mentions
    2. Sentiment analysis - research-agent - Analyze sentiment of brand mentions (positive/neutral/negative)
    3. Awareness measurement - research-agent - Research brand awareness metrics (aided/unaided recall)
    4. Association mapping - content-marketing - Identify brand associations and attributes
    5. Competitive comparison - competitive-market-analyst - Compare brand perception vs competitors
    6. Gap analysis - content-marketing - Identify gaps between desired and actual brand perception
    7. Insight synthesis - research-agent - Extract actionable insights from perception data
    8. Recommendations - content-marketing - Recommend brand positioning and messaging refinements
  • Tags: brand-research, perception-analysis, social-listening, sentiment-analysis, brand-positioning

Academic Research​

31. literature-review-automation​

  • Description: Automated literature review using academic databases, citation analysis, and synthesis
  • Trigger: /literature-review --topic "[research topic]" --sources "[databases]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: research-agent, web-search-researcher, documentation-writer
    • Commands: /research, /smart-research
  • Steps:
    1. Query formulation - research-agent - Develop search queries with Boolean operators and keywords
    2. Database search - web-search-researcher - Search Google Scholar, PubMed, IEEE, ACM, arXiv
    3. Screening - research-agent - Apply inclusion/exclusion criteria to filter relevant papers
    4. Citation collection - web-search-researcher - Extract citations, abstracts, full-text where available
    5. Deduplication - data-engineering - Remove duplicate papers across databases
    6. Citation analysis - research-agent - Analyze citation networks to find seminal works
    7. Synthesis - documentation-writer - Synthesize key findings, methodologies, gaps in literature
    8. Bibliography - documentation-writer - Generate formatted bibliography with annotations
  • Tags: literature-review, academic-research, citation-analysis, synthesis, research-automation

32. citation-network-mapping​

  • Description: Map citation networks to identify influential papers, research clusters, and knowledge evolution
  • Trigger: /map-citations --paper "[seed paper]" --depth "[citation depth]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: research-agent, data-engineering
    • Commands: /research, /visualize-data
  • Steps:
    1. Seed identification - research-agent - Identify seed paper(s) for citation network exploration
    2. Citation extraction - web-search-researcher - Extract forward and backward citations
    3. Network building - data-engineering - Build citation graph with nodes (papers) and edges (citations)
    4. Influence scoring - data-engineering - Calculate citation counts, h-index, PageRank scores
    5. Cluster detection - data-engineering - Apply community detection to find research clusters
    6. Evolution analysis - research-agent - Trace knowledge evolution over time through citation paths
    7. Visualization - frontend-developer - Create interactive citation network visualization
    8. Key papers - research-agent - Identify most influential and foundational papers in network
  • Tags: citation-analysis, network-analysis, academic-research, visualization, bibliometrics

33. research-methodology-design​

  • Description: Design rigorous research methodology for empirical studies, experiments, and investigations
  • Trigger: /design-methodology --research-question "[question]" --approach "[qualitative/quantitative/mixed]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: research-agent, QA-automation, documentation-writer
    • Commands: /research
  • Steps:
    1. Question formulation - research-agent - Refine research questions and hypotheses
    2. Approach selection - research-agent - Choose methodology (qualitative, quantitative, mixed methods)
    3. Study design - research-agent - Design study structure (experimental, observational, case study, survey)
    4. Sampling strategy - research-agent - Define population, sampling method, sample size calculation
    5. Data collection - research-agent - Design data collection instruments (surveys, interviews, experiments)
    6. Analysis plan - data-engineering - Specify statistical/qualitative analysis methods
    7. Validity/reliability - QA-automation - Plan validity and reliability checks, bias mitigation
    8. Documentation - documentation-writer - Write comprehensive methodology documentation for replication
  • Tags: research-methodology, study-design, academic-research, experimental-design, rigor

34. peer-review-coordination​

  • Description: Coordinate peer review process for research papers including reviewer selection and management
  • Trigger: /coordinate-review --paper "[paper]" --reviewers "[list]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: research-agent, project-manager, documentation-writer
    • Commands: /create-plan
  • Steps:
    1. Reviewer identification - research-agent - Identify qualified reviewers based on expertise and conflicts
    2. Invitation - project-manager - Send review invitations with timeline and guidelines
    3. Paper distribution - documentation-writer - Distribute paper to reviewers with review criteria
    4. Deadline tracking - project-manager - Track review progress and send reminders
    5. Review collection - project-manager - Collect completed reviews and consolidate feedback
    6. Synthesis - research-agent - Synthesize reviewer feedback and identify consensus/disagreements
    7. Author response - documentation-writer - Prepare author response to reviewer comments
    8. Revision tracking - project-manager - Coordinate revisions and re-review if needed
  • Tags: peer-review, academic-publishing, review-coordination, quality-assurance, collaboration

35. grant-proposal-research​

  • Description: Research funding opportunities and requirements for grant proposal development
  • Trigger: /research-grants --field "[research field]" --funder "[agencies]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: research-agent, web-search-researcher, documentation-writer
    • Commands: /smart-research, /research
  • Steps:
    1. Funder identification - research-agent - Identify relevant funding agencies (NSF, NIH, DOE, foundations)
    2. Opportunity search - web-search-researcher - Search grant databases and funding announcements
    3. Requirement analysis - research-agent - Extract requirements (eligibility, budget, timeline, format)
    4. Deadline tracking - project-manager - Compile grant deadlines and submission requirements
    5. Success analysis - research-agent - Research previously funded proposals and success factors
    6. Fit assessment - research-agent - Assess fit between research goals and funder priorities
    7. Budget research - business-intelligence-analyst - Research typical budget ranges and allowable costs
    8. Checklist creation - documentation-writer - Create grant application checklist and timeline
  • Tags: grant-research, funding-opportunities, academic-research, proposal-development, research-funding

36. conference-publication-planning​

  • Description: Research conference venues, publication requirements, and create submission strategy
  • Trigger: /plan-publication --topic "[research topic]" --tier "[conference tier]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: research-agent, web-search-researcher, project-manager
    • Commands: /research, /create-plan
  • Steps:
    1. Venue identification - research-agent - Identify relevant conferences by research area and tier
    2. Ranking analysis - research-agent - Research conference rankings (CORE, h5-index, acceptance rates)
    3. Deadline compilation - web-search-researcher - Compile submission deadlines and requirements
    4. Format requirements - research-agent - Extract paper format, length, style requirements
    5. Review process - research-agent - Understand review timeline and notification dates
    6. Acceptance rates - research-agent - Research historical acceptance rates for planning
    7. Submission strategy - project-manager - Create multi-conference submission strategy with backups
    8. Timeline creation - project-manager - Create backward timeline from deadlines to writing milestones
  • Tags: academic-publishing, conference-planning, publication-strategy, research-dissemination, timeline

37. research-data-archiving​

  • Description: Archive research data and materials in repositories for reproducibility and compliance
  • Trigger: /archive-data --dataset "[dataset]" --repository "[repo]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: data-engineering, research-agent, documentation-writer
    • Commands: /build-project
  • Steps:
    1. Repository selection - research-agent - Choose appropriate repository (Zenodo, Figshare, OSF, domain-specific)
    2. Data preparation - data-engineering - Clean, anonymize, format data for archiving
    3. Metadata creation - documentation-writer - Create comprehensive metadata (Dublin Core, DataCite)
    4. Documentation - documentation-writer - Write README, data dictionary, processing scripts
    5. License selection - research-agent - Choose appropriate license (CC0, CC-BY, custom)
    6. Upload - data-engineering - Upload data and documentation to repository
    7. DOI registration - research-agent - Register DOI for persistent citation
    8. Citation - documentation-writer - Create data citation for paper and CV
  • Tags: data-archiving, reproducibility, open-science, research-data, metadata

38. systematic-review-protocol​

  • Description: Develop systematic review protocol with PRISMA methodology and pre-registration
  • Trigger: /systematic-review --topic "[research question]" --databases "[sources]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: research-agent, documentation-writer, QA-automation
    • Commands: /research
  • Steps:
    1. Question formulation - research-agent - Formulate PICO research question (Population, Intervention, Comparison, Outcome)
    2. Protocol development - research-agent - Develop systematic review protocol following PRISMA-P guidelines
    3. Search strategy - research-agent - Design comprehensive search strategy with keywords and Boolean logic
    4. Screening criteria - research-agent - Define inclusion/exclusion criteria and screening process
    5. Quality assessment - QA-automation - Select quality assessment tools (Cochrane, CASP, JBI)
    6. Extraction form - documentation-writer - Design data extraction form and variables
    7. Analysis plan - data-engineering - Plan meta-analysis or narrative synthesis approach
    8. Pre-registration - research-agent - Register protocol in PROSPERO or OSF
  • Tags: systematic-review, PRISMA, research-protocol, evidence-synthesis, meta-analysis

39. research-collaboration-finder​

  • Description: Find potential research collaborators based on expertise, publications, and complementary skills
  • Trigger: /find-collaborators --expertise "[skills needed]" --geography "[region]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: research-agent, web-search-researcher
    • Commands: /research, /smart-research
  • Steps:
    1. Expertise definition - research-agent - Define required expertise, methods, domain knowledge
    2. Database search - web-search-researcher - Search Google Scholar, ResearchGate, ORCID, institutional directories
    3. Publication analysis - research-agent - Analyze publication records for relevant expertise
    4. Citation analysis - research-agent - Identify influential researchers in relevant domains
    5. Collaboration history - research-agent - Research past collaborations and research networks
    6. Geographic filtering - research-agent - Filter by geography if location constraints exist
    7. Fit assessment - research-agent - Assess complementarity of expertise and research interests
    8. Outreach planning - documentation-writer - Draft collaboration inquiry emails with value proposition
  • Tags: research-collaboration, academic-networking, expertise-discovery, team-building, partnerships

40. research-impact-analysis​

  • Description: Analyze research impact using citations, altmetrics, and broader societal influence measures
  • Trigger: /analyze-impact --researcher "[name/ORCID]" --period "[timeframe]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: research-agent, data-engineering
    • Commands: /research, /visualize-data
  • Steps:
    1. Publication gathering - web-search-researcher - Collect publication list from Google Scholar, ORCID, Scopus
    2. Citation metrics - research-agent - Calculate total citations, h-index, i10-index
    3. Temporal analysis - data-engineering - Analyze citation growth over time and trajectory
    4. Altmetrics - web-search-researcher - Gather altmetrics (social media, news, policy mentions)
    5. Field normalization - research-agent - Calculate field-normalized citation impact
    6. Collaboration network - data-engineering - Analyze co-authorship network and reach
    7. Societal impact - research-agent - Research broader impact (patents, products, policy influence)
    8. Visualization - frontend-developer - Create impact dashboard with multiple metrics
  • Tags: research-impact, bibliometrics, altmetrics, citation-analysis, h-index

Customer Research​

41. user-interview-orchestration​

  • Description: Plan and conduct user interviews for qualitative research with recruitment, scripting, and analysis
  • Trigger: /conduct-interviews --segment "[target users]" --count "[number]" or manual
  • Complexity: complex
  • Duration: 30m+ (per interview cycle)
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: research-agent, project-manager, documentation-writer
    • Commands: /create-plan, /research
  • Steps:
    1. Objective definition - research-agent - Define research objectives and key questions
    2. Participant criteria - research-agent - Define target participant criteria and screening questions
    3. Recruitment - project-manager - Recruit participants via email, social, user base, recruitment platforms
    4. Script development - research-agent - Develop semi-structured interview guide with probing questions
    5. Scheduling - project-manager - Schedule interviews with calendar coordination and reminders
    6. Conducting interviews - research-agent - Conduct interviews with recording and note-taking
    7. Transcription - documentation-writer - Transcribe interviews verbatim for analysis
    8. Thematic analysis - research-agent - Code transcripts and extract themes, insights, quotes
  • Tags: user-interviews, qualitative-research, customer-discovery, UX-research, insights

42. survey-design-and-analysis​

  • Description: Design, distribute, and analyze customer surveys for quantitative and qualitative insights
  • Trigger: /create-survey --objective "[research goal]" --audience "[target]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: required, review: recommended
  • Dependencies:
    • Agents: research-agent, data-engineering, frontend-developer
    • Commands: /build-project, /visualize-data
  • Steps:
    1. Research design - research-agent - Define research objectives, hypotheses, variables
    2. Question development - research-agent - Develop survey questions with validated scales (Likert, NPS, etc.)
    3. Survey building - frontend-developer - Build survey in tool (Typeform, Qualtrics, Google Forms)
    4. Testing - QA-automation - Test survey flow, logic, mobile responsiveness
    5. Distribution - project-manager - Distribute survey via email, website, social, paid panels
    6. Response monitoring - project-manager - Monitor response rates and send reminders
    7. Data cleaning - data-engineering - Clean responses, remove duplicates, handle missing data
    8. Statistical analysis - data-engineering - Analyze results with descriptive stats, correlation, regression
    9. Visualization - frontend-developer - Create charts and dashboards for findings
    10. Reporting - documentation-writer - Write research report with insights and recommendations
  • Tags: survey-research, quantitative-research, customer-insights, data-analysis, questionnaire

43. usability-testing-workflow​

  • Description: Conduct usability testing sessions with task scenarios, think-aloud protocol, and analysis
  • Trigger: /usability-test --feature "[feature/product]" --users "[count]" or manual
  • Complexity: moderate
  • Duration: 15-30m (per session)
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: research-agent, QA-automation, frontend-developer
    • Commands: /test, /create-plan
  • Steps:
    1. Test planning - research-agent - Define test objectives, features to test, success criteria
    2. Scenario development - QA-automation - Create realistic task scenarios and user flows
    3. Participant recruitment - project-manager - Recruit participants matching target user personas
    4. Prototype preparation - frontend-developer - Prepare prototype or product for testing
    5. Test facilitation - research-agent - Facilitate usability sessions with think-aloud protocol
    6. Recording - research-agent - Record screen, audio, facial expressions (with consent)
    7. Issue logging - QA-automation - Log usability issues with severity rating
    8. Analysis - research-agent - Analyze success rates, time-on-task, errors, satisfaction
    9. Prioritization - QA-automation - Prioritize issues by frequency, severity, impact
    10. Recommendations - frontend-developer - Generate UX recommendations and wireframe fixes
  • Tags: usability-testing, UX-research, user-testing, qualitative-research, product-validation

44. persona-development-research​

  • Description: Develop data-driven user personas through research, interviews, and behavioral analysis
  • Trigger: /create-personas --product "[product]" --segments "[target segments]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: research-agent, data-engineering, content-marketing
    • Commands: /research, /analyze-segments
  • Steps:
    1. Data collection - research-agent - Collect data from analytics, CRM, surveys, interviews
    2. Segmentation analysis - data-engineering - Apply clustering to identify natural user segments
    3. Qualitative research - research-agent - Conduct interviews to understand motivations, pain points, goals
    4. Pattern identification - research-agent - Identify behavioral patterns, preferences, decision drivers
    5. Persona creation - content-marketing - Create detailed personas with demographics, psychographics, behaviors
    6. Jobs-to-be-done - research-agent - Map jobs-to-be-done framework to each persona
    7. Validation - research-agent - Validate personas with stakeholders and additional user research
    8. Distribution - documentation-writer - Create persona one-pagers and share with product/marketing teams
  • Tags: persona-development, user-research, segmentation, customer-insights, UX-research

45. customer-journey-mapping​

  • Description: Map end-to-end customer journey with touchpoints, emotions, pain points, and opportunities
  • Trigger: /map-journey --persona "[persona]" --stage "[awareness to advocacy]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: research-agent, frontend-developer, content-marketing
    • Commands: /research, /visualize-data
  • Steps:
    1. Persona selection - research-agent - Select persona(s) for journey mapping
    2. Stage definition - research-agent - Define journey stages (awareness, consideration, purchase, retention, advocacy)
    3. Touchpoint identification - research-agent - Identify all touchpoints across channels (web, email, support, product)
    4. Data collection - research-agent - Collect data from analytics, interviews, support tickets, surveys
    5. Action mapping - research-agent - Map customer actions, thoughts, emotions at each stage
    6. Pain point identification - research-agent - Identify friction points and drop-off areas
    7. Opportunity discovery - content-marketing - Identify opportunities to improve experience
    8. Visualization - frontend-developer - Create visual journey map with swim lanes and emotional journey
    9. Workshop - project-manager - Facilitate journey mapping workshop with stakeholders
    10. Action planning - project-manager - Prioritize improvements and assign owners
  • Tags: journey-mapping, customer-experience, UX-research, touchpoint-analysis, CX-optimization

46. nps-feedback-analysis​

  • Description: Analyze Net Promoter Score feedback to identify drivers of satisfaction and detraction
  • Trigger: /analyze-nps --timeframe "[period]" --segment "[segment]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: none
  • Dependencies:
    • Agents: data-engineering, research-agent
    • Commands: /visualize-data, /analyze-sentiment
  • Steps:
    1. Data collection - data-engineering - Collect NPS scores and open-ended feedback from surveys
    2. Score calculation - data-engineering - Calculate NPS (% promoters - % detractors) overall and by segment
    3. Trend analysis - data-engineering - Analyze NPS trends over time and by product/feature
    4. Text analysis - research-agent - Apply NLP to extract themes from open-ended feedback
    5. Driver analysis - data-engineering - Correlate NPS scores with product usage, features, customer attributes
    6. Promoter analysis - research-agent - Analyze what promoters love and why they recommend
    7. Detractor analysis - research-agent - Identify detractor pain points and reasons for low scores
    8. Action planning - project-manager - Create action plan to address detractor issues and amplify promoter strengths
    9. Reporting - documentation-writer - Create NPS report with trends, insights, recommendations
  • Tags: NPS-analysis, customer-satisfaction, feedback-analysis, sentiment-analysis, CX-metrics

47. beta-user-feedback-loop​

  • Description: Orchestrate beta testing feedback collection, analysis, and product iteration loop
  • Trigger: /beta-feedback --cohort "[beta group]" --feature "[feature]" or manual
  • Complexity: moderate
  • Duration: 15-30m (per cycle)
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: research-agent, QA-automation, project-manager
    • Commands: /test, /create-plan
  • Steps:
    1. Beta cohort - project-manager - Recruit and onboard beta users with clear expectations
    2. Feedback channels - project-manager - Set up feedback channels (Slack, email, in-app, surveys)
    3. Usage tracking - data-engineering - Instrument product for usage analytics and behavior tracking
    4. Regular check-ins - project-manager - Schedule weekly check-ins and feedback sessions
    5. Issue tracking - QA-automation - Log bugs, feature requests, usability issues in tracking system
    6. Feedback synthesis - research-agent - Synthesize feedback themes and prioritize issues
    7. Product iteration - frontend-developer - Implement high-priority improvements based on feedback
    8. Communication - project-manager - Communicate changes back to beta users showing impact of feedback
    9. Graduation - project-manager - Graduate beta to general availability with success criteria
  • Tags: beta-testing, feedback-loop, product-iteration, user-validation, continuous-improvement

48. churn-analysis-research​

  • Description: Analyze customer churn patterns to identify causes and retention opportunities
  • Trigger: /analyze-churn --timeframe "[period]" --cohort "[customer segment]" or manual
  • Complexity: complex
  • Duration: 30m+
  • QA Integration: validation: required, review: required
  • Dependencies:
    • Agents: data-engineering, business-intelligence-analyst, research-agent
    • Commands: /visualize-data, /analyze-segments
  • Steps:
    1. Churn definition - business-intelligence-analyst - Define churn criteria (cancellation, inactivity, downgrade)
    2. Cohort analysis - data-engineering - Analyze churn rates by cohort, segment, tenure
    3. Behavioral analysis - data-engineering - Compare behavior of churned vs retained customers
    4. Usage patterns - data-engineering - Identify usage patterns preceding churn (decline in activity, support tickets)
    5. Exit interviews - research-agent - Conduct interviews with churned customers to understand reasons
    6. Survey analysis - research-agent - Analyze cancellation survey feedback for themes
    7. Predictive modeling - data-engineering - Build churn prediction model to identify at-risk customers
    8. Retention strategies - business-intelligence-analyst - Recommend retention interventions and win-back campaigns
    9. Monitoring - data-engineering - Set up churn monitoring dashboard with leading indicators
  • Tags: churn-analysis, retention-research, customer-analytics, predictive-modeling, SaaS-metrics

49. feature-prioritization-research​

  • Description: Research customer needs and preferences to inform feature prioritization and roadmap
  • Trigger: /prioritize-features --candidates "[feature list]" --method "[RICE/Kano/etc]" or manual
  • Complexity: moderate
  • Duration: 15-30m
  • QA Integration: validation: recommended, review: recommended
  • Dependencies:
    • Agents: research-agent, business-intelligence-analyst, project-manager
    • Commands: /create-survey, /research
  • Steps:
    1. Feature candidates - project-manager - Compile list of potential features from backlog, requests, ideas
    2. Research method - research-agent - Choose prioritization framework (RICE, Kano, MaxDiff, conjoint)
    3. Survey design - research-agent - Design survey to measure importance, satisfaction, willingness to pay
    4. User recruitment - project-manager - Recruit representative sample of target users
    5. Data collection - research-agent - Collect responses on feature preferences and priorities
    6. Analysis - data-engineering - Analyze using chosen framework (RICE scores, Kano categorization)
    7. Stakeholder input - project-manager - Gather internal stakeholder input on effort and strategic fit
    8. Prioritization - business-intelligence-analyst - Create prioritized feature roadmap with justification
    9. Validation - research-agent - Validate top priorities with additional user feedback
    10. Communication - documentation-writer - Communicate roadmap to users and internal teams
  • Tags: feature-prioritization, product-research, roadmap-planning, customer-validation, RICE-scoring

50. voice-of-customer-program​

  • Description: Establish systematic voice-of-customer program with continuous feedback collection and analysis
  • Trigger: /voc-program --channels "[feedback sources]" --frequency "[schedule]" or manual
  • Complexity: complex
  • Duration: 30m+ (initial setup)
  • QA Integration: validation: recommended, review: required
  • Dependencies:
    • Agents: research-agent, data-engineering, project-manager, business-intelligence-analyst
    • Commands: /setup-pipeline, /create-plan, /visualize-data
  • Steps:
    1. Program design - research-agent - Design VoC program with multiple feedback channels
    2. Channel setup - project-manager - Set up feedback channels (NPS, surveys, interviews, support, social)
    3. Data integration - data-engineering - Integrate feedback sources into central VoC platform
    4. Tagging taxonomy - research-agent - Create taxonomy for categorizing feedback (feature requests, bugs, praise, pain points)
    5. Analysis automation - data-engineering - Automate sentiment analysis, theme extraction, trending
    6. Reporting - documentation-writer - Create automated VoC reports with insights and trends
    7. Distribution - project-manager - Distribute insights to product, engineering, marketing, support teams
    8. Action tracking - project-manager - Track actions taken in response to customer feedback
    9. Closed-loop - project-manager - Close feedback loop by communicating changes back to customers
    10. Continuous improvement - research-agent - Continuously refine VoC program based on effectiveness
  • Tags: voice-of-customer, feedback-program, customer-insights, continuous-listening, CX-program

Summary Statistics​

Total Workflows: 50 By Category:

  • Web Research: 10 workflows (competitive intelligence, OSINT, trend analysis, market sizing)
  • Data Analytics: 10 workflows (pipelines, visualization, tracking, quality monitoring)
  • Market Intelligence: 10 workflows (market entry, positioning, GTM, pricing optimization)
  • Academic Research: 10 workflows (literature review, peer review, grants, systematic reviews)
  • Customer Research: 10 workflows (interviews, surveys, usability, personas, NPS, churn)

By Complexity:

  • Simple: 0 workflows
  • Moderate: 22 workflows (44%)
  • Complex: 28 workflows (56%)

By Duration:

  • 1-5m: 0 workflows
  • 5-15m: 0 workflows
  • 15-30m: 22 workflows (44%)
  • 30m+: 28 workflows (56%)

QA Integration:

  • Validation Required: 23 workflows (46%)
  • Validation Recommended: 27 workflows (54%)
  • Review Required: 20 workflows (40%)
  • Review Recommended: 25 workflows (50%)
  • Review None: 5 workflows (10%)

Document Version: 1.0.0 Last Updated: 2025-12-12 Maintained By: CODITECT Core Team Related Documentation:

  • Component Activation Guide: docs/guides/COMPONENT-ACTIVATION-GUIDE.md
  • Agent Reference: docs/reference/COMPONENT-REFERENCE.md
  • Multi-Agent Research: commands/multi-agent-research.md