analyze the requirements for a UI / UX agentic system agent with H.P.003-SKILLS, H.P.002-COMMANDS, tools, H.P.004-SCRIPTS, H.P.005-HOOKS following the Anthropic Claude Code agent skill etc process flow
A UI/UX-facing “Claude-style” agent for H.P.003-SKILLS/H.P.002-COMMANDS/tools needs to model the same lifecycle Anthropic uses (discovery → activation → execution → reflection), but surfaced as an interaction contract between H.P.001-AGENTS, frontends, and humans-in-the-loop.123
Below is a requirements breakdown you can turn into specs, schemas, or H.P.007-PROMPTS for your own platform.
Core lifecycle model
Design the UI/UX agent around an explicit agent lifecycle, mirroring Claude Code’s H.P.003-SKILLS flow.21
- Discovery phase
- Skill registry with name + short description discoverable by the model at startup without loading full instructions, to keep context lean.1
- UX affordances for surfacing discoverable H.P.003-SKILLS as suggestions, typeahead, and contextual “available actions” based on current task.
- Activation & permissions
- “Ask to use skill” confirmation step before loading full
SKILL.md/ capability spec into context, with clear UX H.P.007-PROMPTS for user approval, denial, or scoping (e.g., “only for this file/folder/session”).21 - Policy- and context-aware H.P.007-PROMPTS/UI (e.g., show file access scope, network/tool access, data classification labels).
- “Ask to use skill” confirmation step before loading full
- Execution & monitoring
- Reflection & learning
- Surfaces for viewing what the agent learned from corrections (e.g., “autoskill” patterns that update durable preferences from user feedback).4
- UX to accept/reject suggested skill updates or new H.P.003-SKILLS, with change previews and rollback.
Skills, H.P.002-COMMANDS, tools, H.P.004-SCRIPTS, H.P.005-HOOKS (Claude-style)
Model the agent capability layer explicitly, aligned with Claude Code’s skill and hook primitives.12
- Skills
- Each skill has: metadata (name, description, tags), instructions, optional scoped H.P.005-HOOKS, bundled H.P.004-SCRIPTS, and a visibility flag (
user-invocablevs. auto-discovered).1 - UX needs:
- Each skill has: metadata (name, description, tags), instructions, optional scoped H.P.005-HOOKS, bundled H.P.004-SCRIPTS, and a visibility flag (
- Commands
- Thin, user-facing entrypoints mapped to H.P.003-SKILLS/tools (e.g., “Refactor file”, “Generate test suite”), similar to Claude Code H.P.002-COMMANDS lists.56
- Keyboard-first UX: command palette, H.P.009-CONFIGurable shortcuts, toggle to show/hide advanced H.P.002-COMMANDS.
- Command categories for navigation (code, docs, infra, review, data, etc.).
- Tools & programmatic tool use
- Tools exposed to the agent via a code-level API for multi-step orchestration (Anthropic’s “Programmatic Tool Calling”).7
- Requirements:
- Scripts & automation
- Hooks
- Lifecycle H.P.005-HOOKS like
PreToolUse,PostToolUse,Stopscoped to H.P.003-SKILLS or sessions.21 - UX elements:
- Hook H.P.009-CONFIGuration panel per skill (“Run tests on save”, “Auto-format before commit”).
- Event timeline showing hook triggers and outcomes.
- Toggle controls to quickly disable/override H.P.005-HOOKS for a session.
- Lifecycle H.P.005-HOOKS like
UI/UX interaction contract (AG-UI / Magentic-UI style)
Define an explicit agent–UI protocol that your UI agent speaks, so any frontend can implement it.91083
- Event-based protocol
- Generative UI patterns
- Support for static, declarative, and open-ended generative UI layouts, per CopilotKit’s “Generative UI” patterns.93
- Requirements:
- Static: hand-crafted flows for critical tasks with predictable UI, agent mainly filling fields and explaining choices.9
- Declarative: agent outputs structured UI schemas (AG-UI or similar) that the frontend renders into components.39
- Open-ended: conversational flows with mixed tool usage, minimal structural constraints, but with visible tool traces for debugging.89
- Agent ↔ frontend separation
Human-in-the-loop and safety
Build HITL patterns directly into the UI agent, inspired by Magentic-UI and HITL surveys.111210
- Interaction modes
- Co-planning: UI surfaces agent’s task decomposition plan; user can reorder, edit, or veto steps before execution.10
- Co-tasking/multi-tasking: parallel tasks with clearly separated threads and shared state; users can jump between tasks and redirect H.P.001-AGENTS.10
- Action guards: certain high-risk actions trigger mandatory human approvals (file writes, prod API calls, data exports).1210
- HumanInputRequest UX
- Oversight & auditability
- Per-session timeline view: model messages, tool calls, skill activations, H.P.005-HOOKS, human interventions.102
- Exportable logs for compliance, with references to code diffs, data accesses, and decisions.
- Configurable oversight levels per workspace/project (e.g., “always ask before external network calls”).
Architecture & process flow (referenceable in H.P.007-PROMPTS)
An Anthropic-aligned process flow for the UI/UX agent might look like:
- Session start
- Load skill metadata only; present contextual hints in UI based on project type/filetype.1
- User intent capture
- Chat message, command palette invocation, or UI action (e.g., selecting code range).
- Skill discovery & proposal
- Permission and scope negotiation
- Execution loop with H.P.005-HOOKS
- Human-in-loop checkpoints
- Reflection and skill evolution
- Agent proposes updates to H.P.003-SKILLS/preferences; UI shows diff and lets user accept or reject.4
- Completion & logging
Suggested next-step prompt for a deeper iteration
You can paste and specialize this:
“Design a Claude-Code-inspired UI/UX agent that implements the lifecycle (discovery → activation → execution → reflection) for H.P.003-SKILLS, H.P.002-COMMANDS, tools, H.P.004-SCRIPTS, and H.P.005-HOOKS. Use AG-UI-style event schemas and Magentic-UI human-in-the-loop mechanisms (co-planning, action guards, HumanInputRequest). Target a web/IDE hybrid client. Produce:
- JSON schemas for all agent↔UI events (skill activation, tool_call, hook events, human_input_request, layout_update),
- A sequence diagram for a single complex task (e.g., ‘implement feature X’), showing H.P.003-SKILLS, subH.P.001-AGENTS, and HITL checkpoints,
- A TypeScript interface layer that maps Anthropic programmatic tool-calling and skill metadata into the event stream. Cite Anthropic H.P.003-SKILLS/H.P.005-HOOKS docs, AG-UI, CopilotKit Generative UI, and Magentic-UI.”7983101
⁂
Design a Claude-Code-inspired UI/UX agent that implements the lifecycle (discovery → activation → execution → reflection) for H.P.003-SKILLS, H.P.002-COMMANDS, tools, H.P.004-SCRIPTS, and H.P.005-HOOKS. Use AG-UI-style event schemas and Magentic-UI human-in-the-loop mechanisms (co-planning, action guards, HumanInputRequest). Target a web/IDE hybrid client. Produce:
1. JSON schemas for all agent↔UI events (skill activation, tool_call, hook events, human_input_request, layout_update),
2. A sequence diagram for a single complex task (e.g., ‘implement feature X’), showing H.P.003-SKILLS, subH.P.001-AGENTS, and HITL checkpoints,
3. A TypeScript interface layer that maps Anthropic programmatic tool-calling and skill metadata into the event stream. Cite Anthropic H.P.003-SKILLS/H.P.005-HOOKS docs, AG-UI, CopilotKit Generative UI, and Magentic-UI.”
A Claude-Code-inspired UI/UX agent for a web/IDE hybrid should expose a clean event protocol that mirrors the H.P.003-SKILLS lifecycle (discovery → activation → execution → reflection) while embedding AG-UI-style events and Magentic-UI human-in-the-loop mechanisms.[^2_1][^2_2][^2_3][^2_4]
Below is a compact, implementation-ready design focusing on events, a concrete sequence, and a TypeScript interface layer.
1. JSON schemas for agent↔UI events
Event envelope (AG-UI style unified stream).161718
{
"$id": "AgentUIEvent",
"type": "object",
"required": ["id", "type", "timestamp", "role", "payload"],
"properties": {
"id": { "type": "string", "description": "Event UUID" },
"type": {
"type": "string",
"enum": [
"TEXT_MESSAGE_START",
"TEXT_MESSAGE_CONTENT",
"TEXT_MESSAGE_END",
"SKILL_SUGGESTION",
"SKILL_ACTIVATION_REQUEST",
"SKILL_ACTIVATION_RESULT",
"TOOL_CALL_START",
"TOOL_CALL_RESULT",
"HOOK_EVENT",
"HUMAN_INPUT_REQUEST",
"HUMAN_INPUT_RESPONSE",
"LAYOUT_UPDATE",
"PLAN_UPDATE",
"ACTION_GUARD_TRIGGER",
"STATE_DELTA",
"ERROR"
]
},
"role": {
"type": "string",
"enum": ["agent", "user", "system"]
},
"timestamp": { "type": "string", "format": "date-time" },
"correlationId": { "type": "string" },
"payload": { "type": "object" }
}
}
Skill discovery & activation events (Claude H.P.003-SKILLS lifecycle)
Discovery → Activation → Execution mapping Claude H.P.003-SKILLS doc.192021
{
"$id": "SkillSuggestionPayload",
"type": "object",
"required": ["skillId", "name", "description", "confidence"],
"properties": {
"skillId": { "type": "string" },
"name": { "type": "string" },
"description": { "type": "string" },
"confidence": { "type": "number", "minimum": 0, "maximum": 1 },
"reason": { "type": "string" },
"tags": { "type": "array", "items": { "type": "string" } }
}
}
{
"$id": "SkillActivationRequestPayload",
"type": "object",
"required": ["skillId", "name", "description", "scope", "requestedBy"],
"properties": {
"skillId": { "type": "string" },
"name": { "type": "string" },
"description": { "type": "string" },
"requestedBy": { "type": "string", "enum": ["agent", "user"] },
"scope": {
"type": "object",
"properties": {
"files": { "type": "array", "items": { "type": "string" } },
"directories": { "type": "array", "items": { "type": "string" } },
"networkAccess": { "type": "boolean" },
"tools": { "type": "array", "items": { "type": "string" } },
"duration": { "type": "string", "description": "e.g. 'session', 'task', 'persistent'" }
}
},
"riskLevel": {
"type": "string",
"enum": ["low", "medium", "high"]
},
"reason": { "type": "string" }
}
}
{
"$id": "SkillActivationResultPayload",
"type": "object",
"required": ["skillId", "status"],
"properties": {
"skillId": { "type": "string" },
"status": { "type": "string", "enum": ["approved", "denied", "scoped"] },
"approvedScope": { "$ref": "SkillActivationRequestPayload#/properties/scope" },
"denialReason": { "type": "string" }
}
}
Tool call events (Anthropic programmatic tool use aligned with AG-UI TOOL_CALL_*)
Anthropic advanced tool use: structured tool inputs/outputs.171822
{
"$id": "ToolCallStartPayload",
"type": "object",
"required": ["toolCallId", "toolName", "arguments"],
"properties": {
"toolCallId": { "type": "string" },
"toolName": { "type": "string" },
"skillId": { "type": "string" },
"subagentId": { "type": "string" },
"arguments": { "type": "object" },
"actionGuard": {
"type": "object",
"properties": {
"required": { "type": "boolean" },
"reason": { "type": "string" },
"riskLevel": { "type": "string", "enum": ["low", "medium", "high"] }
}
}
}
}
{
"$id": "ToolCallResultPayload",
"type": "object",
"required": ["toolCallId", "status"],
"properties": {
"toolCallId": { "type": "string" },
"status": { "type": "string", "enum": ["success", "error", "cancelled"] },
"output": { "type": "object" },
"logs": { "type": "array", "items": { "type": "string" } },
"error": {
"type": "object",
"properties": {
"message": { "type": "string" },
"code": { "type": "string" }
}
}
}
}
Hook events (Claude H.P.005-HOOKS: PreToolUse, PostToolUse, Stop)
Reflect Claude H.P.005-HOOKS reference and lifecycle.232425
{
"$id": "HookEventPayload",
"type": "object",
"required": ["hookId", "hookType", "phase", "skillId"],
"properties": {
"hookId": { "type": "string" },
"hookType": {
"type": "string",
"enum": ["PreToolUse", "PostToolUse", "Stop", "Custom"]
},
"phase": {
"type": "string",
"enum": ["before", "after", "final"]
},
"skillId": { "type": "string" },
"subagentId": { "type": "string" },
"toolCallId": { "type": "string" },
"scriptName": { "type": "string" },
"status": {
"type": "string",
"enum": ["pending", "running", "success", "error"]
},
"logs": { "type": "array", "items": { "type": "string" } },
"error": {
"type": "object",
"properties": {
"message": { "type": "string" },
"code": { "type": "string" }
}
}
}
}
HumanInputRequest & action guards (Magentic-UI mechanisms)
Co-planning, action approval/action guards, multi-tasking.262728
{
"$id": "HumanInputRequestPayload",
"type": "object",
"required": ["requestId", "reason", "schema", "mode"],
"properties": {
"requestId": { "type": "string" },
"reason": { "type": "string" },
"mode": {
"type": "string",
"enum": ["co_planning", "action_guard", "answer_verification", "disambiguation"]
},
"taskId": { "type": "string" },
"planSnapshot": { "type": "object" },
"schema": {
"type": "object",
"description": "JSON Schema or AG-UI compatible form definition"
},
"suggestedOptions": {
"type": "array",
"items": { "type": "string" }
},
"timeoutSeconds": { "type": "number" },
"allowPartial": { "type": "boolean" }
}
}
{
"$id": "HumanInputResponsePayload",
"type": "object",
"required": ["requestId", "status"],
"properties": {
"requestId": { "type": "string" },
"status": { "type": "string", "enum": ["approved", "rejected", "modified"] },
"input": { "type": "object" },
"comments": { "type": "string" }
}
}
{
"$id": "ActionGuardTriggerPayload",
"type": "object",
"required": ["guardId", "reason", "riskLevel"],
"properties": {
"guardId": { "type": "string" },
"reason": { "type": "string" },
"riskLevel": { "type": "string", "enum": ["medium", "high"] },
"relatedToolCallId": { "type": "string" },
"proposedAction": { "type": "object" }
}
}
Layout & plan updates (AG-UI generative UI + Magentic-UI co-planning)
Generative UI & plan editing for co-planning/co-tasking.292718
{
"$id": "LayoutUpdatePayload",
"type": "object",
"required": ["layoutId", "components"],
"properties": {
"layoutId": { "type": "string" },
"mode": {
"type": "string",
"enum": ["static", "declarative", "generative"]
},
"components": {
"type": "array",
"items": {
"type": "object",
"required": ["id", "componentType"],
"properties": {
"id": { "type": "string" },
"componentType": { "type": "string" },
"props": { "type": "object" },
"bindings": { "type": "object" }
}
}
}
}
}
{
"$id": "PlanUpdatePayload",
"type": "object",
"required": ["planId", "steps"],
"properties": {
"planId": { "type": "string" },
"mode": {
"type": "string",
"enum": ["initial", "revised", "user_edited"]
},
"steps": {
"type": "array",
"items": {
"type": "object",
"required": ["id", "description", "status"],
"properties": {
"id": { "type": "string" },
"description": { "type": "string" },
"status": {
"type": "string",
"enum": ["pending", "in_progress", "completed", "blocked"]
},
"skillId": { "type": "string" },
"subagentId": { "type": "string" },
"requiresApproval": { "type": "boolean" }
}
}
}
}
}
2. Sequence diagram for “implement feature X”
Textual sequence diagram showing lifecycle + HITL; aligns with Claude H.P.003-SKILLS phases and Magentic-UI mechanisms.27282129
Actors
- User
- IDE/Web Client
- UI Agent (this component)
- Orchestrator / Main Agent
- Subagent(s) (e.g., “FrontendAgent”, “TestAgent”)
- Tools (VCS, TestRunner, Linter, API client, etc.)
Flow
- Discovery
1.1 User selects code and runs “Implement feature X” via command palette in IDE/web.
1.2 IDE sends
TEXT_MESSAGE_START/CONTENT/ENDevent with user intent to UI Agent → Orchestrator.2917 1.3 Orchestrator inspects H.P.003-SKILLS metadata (name+description only) and emitsSKILL_SUGGESTIONforfeature_implementation_skillandapi_consult_skill.21 - Skill activation (with HITL)
2.1 UI Agent forwards
SKILL_ACTIVATION_REQUESTto client, requesting use offeature_implementation_skillwith scope (repo path, network disabled, test tools enabled).1921 2.2 Client presents modal; user approves with narrowed scope (no network, only local tests) → sendsSKILL_ACTIVATION_RESULT(status=approved, scoped).2729 2.3 Orchestrator loads fullSKILL.mdinto context and may spawn subH.P.001-AGENTS for frontend/backend impacts.2021 - Co-planning (Magentic-UI)
3.1 Orchestrator generates an execution plan: analyze requirements → modify API → update UI → write tests → run tests → create diff.2627
3.2 UI Agent emits
PLAN_UPDATE(mode=initial) +LAYOUT_UPDATEfor a plan panel and diff viewer.2927 3.3 Client shows editable checklist; user reorders steps, adds “update docs”, and toggles “requiresApproval” for “run migrations”. 3.4 Client sendsPLAN_UPDATE(mode=user_edited) back to orchestrator (co-planning completed).2827 - Execution with tools, H.P.005-HOOKS, and subH.P.001-AGENTS
4.1 Orchestrator dispatches “update UI” to
FrontendAgentsubagent; UI Agent getsTEXT_MESSAGE_*andTOOL_CALL_STARTforapply_patchtool.2129 4.2PreToolUsehook fires forapply_patch(e.g., run formatter/linter) →HOOK_EVENTwith phase=before.2523 4.3TOOL_CALL_RESULTreturns patch success, plus logs;PostToolUsehook runs tests if lightweight.2423 4.4 In parallel,TestAgentsubagent runsTestRunnertool; tool logs stream viaTOOL_CALL_RESULTandTEXT_MESSAGE_CONTENTevents.1729 - Action guards & HumanInputRequest
5.1 Plan has step “run DB migration” flagged
requiresApproval=true; orchestrator is about to calldb_migratetool. 5.2 UI Agent emitsACTION_GUARD_TRIGGERwith reason and high risk level.2827 5.3 Simultaneously, it sendsHUMAN_INPUT_REQUEST(mode=action_guard) with schema requiring checkbox confirmation and environment selection (e.g., staging vs prod).27 5.4 User selects “staging only” and approves → client returnsHUMAN_INPUT_RESPONSE(approved, input=staging) → orchestrator callsdb_migrateagainst staging only.2628 - Reflection & wrap-up
6.1 Once all steps complete, orchestrator emits
PLAN_UPDATEwith all stepscompletedand a finalTEXT_MESSAGE_ENDsummary.29 6.2 Hooks with typeStopfire to clean up temp state and notify user (e.g., notification hook) →HOOK_EVENT(phase=final).2325 6.3 UI Agent presents diff view + “learn from corrections” toggle; user tweaks output and selects “remember style for this repo”. 6.4 Orchestrator updates skill preferences (e.g., repo-specific guidelines) and logs aSTATE_DELTAevent for audit/long-term memory.2821
3. TypeScript interface layer for Anthropic tools & H.P.003-SKILLS → event stream
This layer sits between an Anthropic-style orchestrator (H.P.003-SKILLS, programmatic tools) and AG-UI-compatible event consumers.18221621
Core TypeScript types
// Shared envelope
export type AgentEventType =
| "TEXT_MESSAGE_START"
| "TEXT_MESSAGE_CONTENT"
| "TEXT_MESSAGE_END"
| "SKILL_SUGGESTION"
| "SKILL_ACTIVATION_REQUEST"
| "SKILL_ACTIVATION_RESULT"
| "TOOL_CALL_START"
| "TOOL_CALL_RESULT"
| "HOOK_EVENT"
| "HUMAN_INPUT_REQUEST"
| "HUMAN_INPUT_RESPONSE"
| "LAYOUT_UPDATE"
| "PLAN_UPDATE"
| "ACTION_GUARD_TRIGGER"
| "STATE_DELTA"
| "ERROR";
export interface AgentUIEvent<TPayload = any> {
id: string;
type: AgentEventType;
role: "agent" | "user" | "system";
timestamp: string;
correlationId?: string;
payload: TPayload;
}
Skill metadata (Claude H.P.003-SKILLS + H.P.005-HOOKS).2321
export interface SkillMetadata {
id: string;
name: string;
description: string;
tags?: string[];
userInvocable: boolean;
H.P.005-HOOKS?: {
preToolUse?: string[]; // hook IDs or script names
postToolUse?: string[];
stop?: string[];
};
}
Tool schema aligned with Anthropic programmatic tool calling (JSON-schema-like).22
export interface ToolSchema {
name: string;
description: string;
inputSchema: Record<string, any>;
outputSchema?: Record<string, any>;
skillId?: string;
riskLevel?: "low" | "medium" | "high";
}
Mapping Anthropic tool calls → events
Suppose Anthropic SDK gives you tool calls like:
export interface AnthropicToolCall {
id: string;
name: string;
arguments: Record<string, any>;
}
Adapter:
export function toToolCallStartEvent(
call: AnthropicToolCall,
opts: { skillId?: string; subagentId?: string; riskLevel?: "low" | "medium" | "high" }
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "TOOL_CALL_START",
role: "agent",
timestamp: new Date().toISOString(),
correlationId: call.id,
payload: {
toolCallId: call.id,
toolName: call.name,
skillId: opts.skillId,
subagentId: opts.subagentId,
arguments: call.arguments,
actionGuard: opts.riskLevel && opts.riskLevel !== "low"
? {
required: true,
reason: "High-risk tool call requires human approval",
riskLevel: opts.riskLevel
}
: { required: false }
}
};
}
Tool result mapping:
export interface AnthropicToolResult {
id: string;
status: "success" | "error";
output?: any;
error?: { message: string; code?: string };
logs?: string[];
}
export function toToolCallResultEvent(
result: AnthropicToolResult
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "TOOL_CALL_RESULT",
role: "agent",
timestamp: new Date().toISOString(),
correlationId: result.id,
payload: {
toolCallId: result.id,
status: result.status,
output: result.output,
logs: result.logs ?? [],
error: result.error
}
};
}
Mapping Claude H.P.003-SKILLS lifecycle → events
At startup, only name/description are loaded (discovery).1921
export function suggestSkills(
H.P.003-SKILLS: SkillMetadata[],
context: { intent: string }
): AgentUIEvent[] {
// Placeholder scoring; in practice based on embeddings/rules.
return H.P.003-SKILLS.map((skill) => ({
id: crypto.randomUUID(),
type: "SKILL_SUGGESTION",
role: "agent",
timestamp: new Date().toISOString(),
payload: {
skillId: skill.id,
name: skill.name,
description: skill.description,
confidence: 0.75,
reason: `Matches intent: ${context.intent}`,
tags: skill.tags ?? []
}
}));
}
When agent decides to activate a skill, request user confirmation (Claude-style activation).2021
export interface SkillActivationScope {
files?: string[];
directories?: string[];
networkAccess?: boolean;
tools?: string[];
duration?: "session" | "task" | "persistent";
}
export function toSkillActivationRequestEvent(
skill: SkillMetadata,
scope: SkillActivationScope,
reason: string
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "SKILL_ACTIVATION_REQUEST",
role: "agent",
timestamp: new Date().toISOString(),
payload: {
skillId: skill.id,
name: skill.name,
description: skill.description,
requestedBy: "agent",
scope,
riskLevel: "medium",
reason
}
};
}
Client’s decision → event back in:
export interface SkillActivationDecision {
skillId: string;
approved: boolean;
approvedScope?: SkillActivationScope;
denialReason?: string;
}
export function fromSkillActivationDecision(
decision: SkillActivationDecision
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "SKILL_ACTIVATION_RESULT",
role: "user",
timestamp: new Date().toISOString(),
payload: {
skillId: decision.skillId,
status: decision.approved ? "approved" : "denied",
approvedScope: decision.approvedScope,
denialReason: decision.denialReason
}
};
}
Human-in-the-loop (Magentic-UI co-planning & action guards)
Emit HumanInputRequest for plan editing and guards.262728
export function emitCoPlanningRequest(
taskId: string,
planSnapshot: any
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "HUMAN_INPUT_REQUEST",
role: "agent",
timestamp: new Date().toISOString(),
payload: {
requestId: crypto.randomUUID(),
reason: "Review and edit execution plan (co-planning)",
mode: "co_planning",
taskId,
planSnapshot,
schema: {
type: "object",
properties: {
steps: { type: "array" },
requiresApprovalIds: { type: "array", items: { type: "string" } }
}
},
timeoutSeconds: 0,
allowPartial: true
}
};
}
Guarded action:
export function emitActionGuard(
relatedToolCallId: string,
reason: string,
riskLevel: "medium" | "high"
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "ACTION_GUARD_TRIGGER",
role: "agent",
timestamp: new Date().toISOString(),
payload: {
guardId: crypto.randomUUID(),
reason,
riskLevel,
relatedToolCallId,
proposedAction: {}
}
};
}
UI response:
export interface HumanInputResponse {
requestId: string;
status: "approved" | "rejected" | "modified";
input?: any;
comments?: string;
}
export function toHumanInputResponseEvent(
resp: HumanInputResponse
): AgentUIEvent {
return {
id: crypto.randomUUID(),
type: "HUMAN_INPUT_RESPONSE",
role: "user",
timestamp: new Date().toISOString(),
payload: resp
};
}
This gives you:
- A Claude-accurate lifecycle mapped into AG-UI-style events (streaming, tool events, layout updates).16182129
- Magentic-UI-inspired HITL flows via co-planning, action guards, and HumanInputRequest semantics.272826
- A concrete TypeScript layer to bridge Anthropic H.P.003-SKILLS and programmatic tool calls into a single event bus suitable for a web/IDE hybrid client.
⁂
Define AG-UI event schema examples for Skill discovery and activation
Skill discovery and activation in AG-UI are modeled as plain JSON events on the same stream as text, tools, and state updates. Below are focused examples you can adopt or adapt.323334
Event envelope (AG-UI style)
{
"type": "SKILL_SUGGESTION", // custom event type
"threadId": "thread-123",
"runId": "run-456",
"timestamp": "2026-01-19T19:32:00.000Z",
"payload": { /* see below */ }
}
AG-UI conventions to keep: uppercase type, camelCase fields, threadId / runId for correlation.353332
Skill discovery events
1) Agent suggests candidate H.P.003-SKILLS (discovery)
{
"type": "SKILL_SUGGESTION",
"threadId": "thread-123",
"runId": "run-456",
"timestamp": "2026-01-19T19:32:00.000Z",
"payload": {
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"description": "Plans and implements multi-file code changes for a requested feature.",
"confidence": 0.86,
"reason": "User asked to 'add a bulk edit feature' in this repository.",
"tags": ["code", "refactor", "multi-file"]
}
}
This mirrors AG-UI’s pattern of structured, typed events where payloads carry domain-specific data.3432
2) Snapshot of discoverable H.P.003-SKILLS (for UI palette)
{
"type": "SKILLS_SNAPSHOT",
"threadId": "thread-123",
"runId": "run-456",
"timestamp": "2026-01-19T19:32:05.000Z",
"payload": {
"H.P.003-SKILLS": [
{
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"description": "Plans and implements multi-file code changes.",
"userInvocable": true,
"tags": ["code", "planning"]
},
{
"skillId": "test_generation_skill",
"name": "Generate Tests",
"description": "Creates unit and integration tests for selected code.",
"userInvocable": true,
"tags": ["tests", "quality"]
}
]
}
}
This uses AG-UI’s snapshot/delta pattern applied to a “H.P.003-SKILLS” substate.32
Skill activation events
3) Agent requests activation (needs user approval)
{
"type": "SKILL_ACTIVATION_REQUEST",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:00.000Z",
"payload": {
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"description": "Plans and implements multi-file code changes.",
"requestedBy": "agent",
"scope": {
"files": ["src/components/**/*"],
"directories": ["src/H.P.005-HOOKS"],
"networkAccess": false,
"tools": ["git_diff", "apply_patch", "run_tests"],
"duration": "task"
},
"riskLevel": "medium",
"reason": "Best-suited skill for implementing the requested feature in the selected project."
}
}
This fits AG-UI’s “stateful structured event” style and is compatible with Magentic-style HITL approvals.3634
4) User responds to activation request
{
"type": "SKILL_ACTIVATION_RESULT",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:20.000Z",
"payload": {
"skillId": "feature_implementation_skill",
"status": "approved",
"approvedScope": {
"files": ["src/components/**/*"],
"directories": [],
"networkAccess": false,
"tools": ["git_diff", "apply_patch"],
"duration": "task"
},
"denialReason": null
}
}
This reuses the same threadId/runId and acts like a small “state delta” confirming the skill H.P.009-CONFIGuration.3332
5) Optional: activation state delta (for UI state sync)
{
"type": "STATE_DELTA",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:21.000Z",
"payload": {
"H.P.003-SKILLS": {
"active": [
{
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"scope": {
"files": ["src/components/**/*"],
"networkAccess": false,
"tools": ["git_diff", "apply_patch"],
"duration": "task"
}
}
]
}
}
}
This follows AG-UI’s snapshot/delta state management guidance while keeping skill activation visible to all clients.3732
⁂
Explain required AG-UI fields for skill discovery events
For skill discovery events in an AG-UI-based system, the required fields are the standard AG-UI event envelope plus a minimal, well-typed payload describing the skill suggestion.4849
Below is a concise breakdown.
Base AG-UI event fields
Every skill discovery event should include the core AG-UI event identifiers so frontends can route and correlate it.505148
- type
- threadId
- Required. Conversation/session identifier so the UI knows which chat/editor context this discovery belongs to.48
- runId
- timestamp
- payload
- Required. JSON object containing the domain-specific data for skill discovery (see next section).48
These mirror AG-UI’s base event structure and the RunStarted / text / tool events (same identifiers, different type).5448
Required payload fields for SKILL_SUGGESTION
For a single skill discovery event (one candidate skill), the payload should minimally contain:4948
- skillId
- Required. Stable, unique identifier for the skill, used later for activation, logging, and state deltas.
- Should not change across runs so the UI can remember preferences per skill.
- name
- Required. Short, human-readable label (e.g., “Implement Feature”) used in menus, chips, and tooltips.55
- description
- Required. One or two-sentence explanation of what the skill does, helping both agent and user understand when to apply it.55
- confidence
- Required for ranking. Float in $[0, 1]$ indicating how strongly the agent believes this skill fits the current intent; UIs use this for ordering or thresholding.
Recommended (but optional) fields that complete a practical discovery payload:
- reason
- tags
- Light categorization (e.g.,
["code", "tests", "refactor"]) for filtering and grouping in palettes or sidebars.55
- Light categorization (e.g.,
This matches AG-UI’s guidance: custom events keep the base envelope and use a clearly documented value/payload schema for domain-specific data.4948
Required payload fields for SKILLS_SNAPSHOT (list discovery)
For a snapshot event listing multiple discoverable H.P.003-SKILLS (e.g., to populate a command palette), the payload should contain:4948
- H.P.003-SKILLS
- Required. Array of objects, each with the same minimal fields as above:
skillId(required)name(required)description(required)
- Optional per skill:
userInvocable,tags,defaultEnabled, etc.
- Required. Array of objects, each with the same minimal fields as above:
This aligns with AG-UI’s “state snapshot / delta” pattern, where STATE_DELTA or domain-specific snapshots update shared state on the client, and the UI can locally maintain a H.P.003-SKILLS registry tied to the agent run.505448
⁂
Show AG-UI events for a Skill activation confirmation flow
A skill activation confirmation flow in AG-UI is just a small, custom event protocol built on top of AG-UI’s standard patterns: agent proposes → UI renders confirmation UI → user decision → state delta.636465
Below is a concrete, minimal flow you can drop into your design.
1) Agent proposes a skill (request activation)
{
"type": "SKILL_ACTIVATION_REQUEST",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:00.000Z",
"payload": {
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"description": "Plans and implements multi-file code changes.",
"requestedBy": "agent",
"scope": {
"files": ["src/components/**/*"],
"directories": ["src/H.P.005-HOOKS"],
"networkAccess": false,
"tools": ["git_diff", "apply_patch", "run_tests"],
"duration": "task"
},
"riskLevel": "medium",
"reason": "Best-suited skill for the requested feature in this repository."
}
}
- Pattern: custom event type with structured payload, following AG-UI conventions (
threadId,runId, ISO timestamp).6663 - UI: shows modal/card with description, scope, risk, and Approve/Deny/Modify options.67
2) UI emits the user’s decision back to the agent
{
"type": "SKILL_ACTIVATION_RESULT",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:20.000Z",
"payload": {
"skillId": "feature_implementation_skill",
"status": "approved",
"approvedScope": {
"files": ["src/components/**/*"],
"directories": [],
"networkAccess": false,
"tools": ["git_diff", "apply_patch"],
"duration": "task"
},
"denialReason": null
}
}
- Pattern: a simple response event on the same
threadId/runId, analogous to a state-confirmation callback.6563 - If the user denies:
status: "denied"anddenialReasonset accordingly.
3) Agent publishes a state delta to mark the skill as active
{
"type": "STATE_DELTA",
"threadId": "thread-123",
"runId": "run-789",
"timestamp": "2026-01-19T19:33:21.000Z",
"delta": [
{
"op": "add",
"path": "/H.P.003-SKILLS/active/-",
"value": {
"skillId": "feature_implementation_skill",
"name": "Implement Feature",
"scope": {
"files": ["src/components/**/*"],
"networkAccess": false,
"tools": ["git_diff", "apply_patch"],
"duration": "task"
}
}
}
]
}
- Pattern: AG-UI’s JSON Patch–based
STATE_DELTAto synchronize active H.P.003-SKILLS in the shared state model.6465 - UI: updates “Active H.P.003-SKILLS” list, badges, and command palette context automatically.
This three-event pattern (request → result → state delta) fits cleanly into AG-UI’s event model and gives you a reusable confirmation flow for any Claude-style skill activation.
⁂
LIST the SKILLS a UI / UX agent must have be very comprehensive and detailed do a web search to help determine the requirements
A UI/UX agent in an agentic system needs a broad skill surface: interaction design, safety/HITL, stateful AG-UI communication, and Claude-style design intelligence for multi-platform apps. Below is a comprehensive H.P.003-SKILLS list grouped into capability domains.80818283
1. UX research & user modeling H.P.003-SKILLS
- Persona synthesis skill
- Task & journey mapping skill
- Problem framing & UX brief skill
2. Information architecture & navigation H.P.003-SKILLS
- IA & sitemap design skill
- Proposes navigation structures (global nav, local nav, content hierarchy) based on product scope and personas.84
- Creates structured IA artifacts (trees, menus) that can be turned into components.
- Flow restructuring & simplification skill
- Identifies unnecessary steps, branching, and confusing flows.
- Suggests alternatives (wizards vs inline steps, progressive disclosure, shortcuts).84
- Multi-device navigation adaptation skill
3. Layout, interaction patterns, and generative UI H.P.003-SKILLS
- ASCII / low-fidelity layout skill
- Produces ASCII-first screen layouts and component structures for rapid iteration (fields, buttons, list views, states) before pixels.88
- Supports progressive fidelity: layout → component metadata → design tokens → high-fidelity outputs.
- Component system & pattern library skill
- Generative UI schema skill (AG-UI aware)
- Microinteraction & feedback skill
4. Visual design & design system H.P.003-SKILLS
- Design tokens & theming skill
- Component skinning & theming skill
- Applies design tokens to existing component structures while preserving semantics and accessibility.
- Supports multiple themes (light/dark/high-contrast) with clear switching rules.89
- Cross-platform UI adaptation skill
5. Content design, UX writing, and onboarding H.P.003-SKILLS
- UX writing & microcopy skill
- Generates concise labels, helper texts, error messages, and empty states aligned with tone of voice guidelines.84
- Adapts language for different audiences (end-users vs operators vs engineers).
- Onboarding & education skill
- Notification, alerts, and escalation skill
6. Accessibility & inclusive design H.P.003-SKILLS
- Accessibility audit skill
- Reviews UI specs or code and flags issues: color contrast, focus order, keyboard traps, ARIA misuse, motion sensitivity.84
- Suggests concrete fixes and improved patterns.
- Accessible interaction pattern skill
- Inclusive content & localization skill
- Detects non-inclusive language, culture-specific assumptions, and localization pitfalls.
- Generates localized content variants and layout adaptations for RTL or long text.84
7. Human-in-the-loop and safety UX H.P.003-SKILLS (Magentic-UI / oversight)
- Co-planning UI skill
- Co-tasking / multi-tasking UI skill
- Action guard & approval flow skill
- Answer verification & review skill
8. AG-UI protocol & state management H.P.003-SKILLS
- Event modeling & schema design skill
- Stateful UI synchronization skill
- Tool visualization & traceability skill
9. Agent behavior explanation & trust-building H.P.003-SKILLS
- Intent & rationale explanation skill
- Risk & uncertainty communication skill
- Change/diff explanation skill
10. Implementation and handoff H.P.003-SKILLS (code, design tools)
- Design-to-code handoff skill
- Design tool integration skill
- Agent-in-IDE UX skill
11. Meta-H.P.003-SKILLS: orchestration, evaluation, and evolution
- UX evaluation & heuristic review skill
- Applies known UX heuristics and platform guidelines to existing designs and produces prioritized issue lists.84
- Suggests A/B test ideas or metrics.
- Telemetry & feedback loop design skill
- Skill composition & pipeline skill
This list gives you a comprehensive skill surface for a Claude-style UI/UX agent embedded in AG-UI/Magentic-UI H.P.006-WORKFLOWS, spanning discovery, design, interaction, safety, and continuous improvement.8090838284
⁂
List technical H.P.003-SKILLS required for a UI/UX designer with examples
A UI/UX designer’s technical skill set spans research, design, prototyping, handoff, and evaluation. Below is a concise, implementation-focused list with concrete examples.
1. Wireframing & prototyping
- Low- and high-fidelity wireframing
- Interactive prototyping
Example: Taking a rough feature idea and producing low-fi user flows in Figma, then a high-fi clickable prototype for user testing.10199
2. Visual & UI design
- Layout, spacing, and visual hierarchy
- Color, typography, and iconography
- Design systems and reusable components
Example: Creating a reusable card component system in Figma with variants (states, densities) tied to a design token set.104102
3. UX research & information architecture
- UX research methods
- Information architecture & sitemaps
- User flows & journey mapping
Example: Running a remote usability test on an onboarding flow and updating the sitemap and flow diagrams based on observed drop-off points.100101
4. Tools, front-end awareness & collaboration
- Design tools
- Basic front-end understanding
- Handoff & documentation
Example: Designing a responsive dashboard and collaborating with developers by providing Figma specs, CSS-friendly spacing/typography scales, and component states.102104
5. Testing, analytics & accessibility
- Usability testing & iteration
- Analytics & experiment literacy
- Accessibility (a11y) fundamentals
Example: Adjusting color contrast and focus states after running an accessibility check and a small usability test with keyboard-only navigation.105102
⁂
Examples of technical UI design tools and when to use them
Here are key technical UI design tools, grouped by stage, with when to use each.109110111112
Wireframing & early exploration
- Balsamiq
- FigJam / Miro (whiteboarding)
Visual design & component systems
- Figma
- Sketch
- Adobe Photoshop / Illustrator
Prototyping & interaction design
- Figma prototypes / Smart Animate
- Axure RP
- ProtoPie / Framer
Testing, validation & collaboration
- InVision / Marvel / Proto.io / Justinmind
- Maze (and similar remote testing platforms)
- Use to run unmoderated usability tests, task success analyses, and surveys on your prototypes.113
- Helpful when you need quick, quantitative validation on design iterations.
- Zeplin / Figma Inspect / Storybook integration
When to choose what (rules of thumb)
- Early idea / buy-in: Balsamiq or FigJam for rough flows and low-fi wireframes.112109
- Main product UI and systems: Figma or Sketch as the core design environment.111109110
- Complex logic-heavy prototypes: Axure RP when you need conditions, variables, and rich interactions.109111
- Motion-critical experiences: ProtoPie or Framer to explore advanced micro-interactions.112109
- Validation and handoff: InVision/Marvel for review, Maze for testing, Zeplin/Figma Inspect for developer-ready specs.113110
⁂
When to use Figma vs Sketch for UI design
Use Figma when you need cross‑platform, real‑time collaboration and integrated design → prototype → feedback H.P.006-WORKFLOWS; use Sketch when you are a Mac‑centric team optimizing for a highly customizable, plugin‑driven vector design environment.117118119
When Figma is the better choice
- Distributed or cross‑platform teams
- Heavy collaboration and fast iteration
- All‑in‑one workflow (design + prototype + feedback)
When Sketch is the better choice
- Mac‑only design teams
- Plugin‑heavy, craft‑focused H.P.006-WORKFLOWS
- Individual or small teams focused on detailed UI craft
Quick rule of thumb
- Prefer Figma for: multi‑platform, collaborative product teams, design systems, live reviews, and integrated prototyping.117119118
- Prefer Sketch for: Mac‑only teams or individual designers wanting a deeply customizable, plugin‑driven tool for precise visual/UI work.119118
⁂
Can I switch design libraries from Sketch to Figma easily
You can import Sketch files into Figma, but “switching” a full design library is not a one‑click, lossless operation; expect to rebuild key parts of your system (styles, components) to do it well.127128129130
What transfers well
- Artboards, layers, vectors, images
- Symbols → components
Implication: For mockups and one‑off screens, importing and cleaning up may be “good enough.”
What does not migrate cleanly
- Styles (colors, text, effects)
- Complex design systems
Implication: For a serious library/design system, treat Figma as a fresh system and use Sketch only as visual/reference input.134127133
Recommended migration approach
- Import for reference, rebuild for quality
- Prioritize foundations and high‑leverage components
Rule of thumb:
- Small projects / few screens → import
.sketchfiles and fix issues by hand. - Large, library‑driven products → import as reference and plan a structured rebuild in Figma for a cleaner, future‑proof system.128133130127
⁂
Best practices to rebuild a Sketch design system in Figma
Rebuilding a Sketch design system in Figma works best if you treat it as a re‑architecture, not a direct import. Plan to rebuild foundations (tokens, components, libraries) and use Sketch only as a reference layer.142143144145
1. Make a migration plan first
- Inventory and prioritize
- Separate “old” from “new”
2. Rebuild foundations as Figma-native tokens & styles
- Colors and typography as tokens/variables
- Spacing, radius, elevation
Goal: get a clean, Figma-native token layer that may differ structurally from Sketch but preserves visual identity.147148
3. Design components for Auto Layout & variants (not direct symbol copies)
- Re-model core components using Auto Layout
- Use variants instead of many similar symbols
- Minimize detaching instances
Rule: design each component as if it will become more complex later; Auto Layout + variants are your primary tools.148150151
4. Structure libraries and files for scale
- One core system file + product-level component files
- Use Figma shared libraries deliberately
- Organize components logically
5. Migrate screens incrementally and refactor in place
- Import for reference, then swap to Figma components
- Phase-by-phase migration
- Validate with design system analytics and consumers
6. Document usage and governance early
- Usage guidelines & examples
- Change management & versioning
If you want, next step can be: a concrete Figma file structure + naming convention tailored to your stack (e.g., mapping tokens/components to your React design system and Tailwind H.P.009-CONFIG).
⁂
IS THERE AN OPEN SOURCE FIGMA like toolset that runs in a browser with screenshot and file management capability?
Yes. Penpot is the main open‑source, Figma‑like design tool that runs in the browser and supports multi‑file design work and asset management.158159160
Penpot (primary candidate)
- What it is
- Why it’s Figma-like
- File & asset management
- Organizes work in projects/files with pages and components, plus export in multiple formats (SVG, PNG, PDF, etc.).160159
- Open formats make it straightforward to integrate with external screenshot/asset pipelines or custom storage, and it can ingest/export SVG and other design assets for reuse.159160
Other open or free browser-based options (less “Figma‑complete”)
- Uizard – free, browser-based tool focused on fast idea-to-UI with AI, but not open source and more aimed at rapid prototyping than full design systems.162
- Alva – open-source design tool that works with live React components, but more specialized for design systems than general Figma replacement and less mature as a full UI editor.161
Screenshot and workflow considerations
- None of these are “screenshot managers” in the sense of a dedicated capture tool, but:
- Penpot’s browser UI works well with standard OS/browser screenshot tools, and its asset exports can act as “canonical” snapshots in a repo or asset pipeline.160159
- For full screenshot and file lifecycle management, you’d typically pair Penpot with your own storage (Git, object storage, DAM) or a separate screenshot system while using Penpot as the design/editor layer.
If you describe your workflow (self‑hosted vs SaaS, need for programmatic asset export, multi‑tenant constraints), a tailored stack diagram (e.g., Penpot + S3/GCS + Git + CI) can be sketched next.
⁂
Compare Penpot vs Figma feature-by-feature
Penpot and Figma overlap heavily on core UI design/prototyping, but diverge on ecosystem, extensibility, and control. Figma is still ahead on polish, AI, and integrations; Penpot leads on openness, self‑hosting, and standards‑based output.168169170171172173174175
Core design & layout
| Area | Figma | Penpot |
|---|---|---|
| Vector & layout | Advanced vector tools, frames, constraints, Auto Layout with robust responsive behavior.170173 | Advanced vector editor with components, constraints, and CSS Flex/Grid‑style layout.170171175 |
| Components & libraries | Mature components, variants, component properties, nested instances, strong library tooling.170173 | Components, variants, design tokens; still closing the gap but covers most common system use cases.171169 |
| Design systems | Rich design‑system H.P.006-WORKFLOWS (variables, tokens, Dev Mode, analytics).176172173 | Components + variants + tokens with open formats (CSS/SVG/HTML), good for code‑aligned systems.171177 |
Implication: For large, complex design systems with heavy variant use and Dev Mode H.P.006-WORKFLOWS, Figma is more mature; for standards‑oriented systems where CSS/SVG fidelity matters, Penpot is attractive.169171177
Prototyping & interactions
| Area | Figma | Penpot |
|---|---|---|
| Prototyping depth | Advanced prototyping with interactive components, smart animations, conditions, and now AI‑assisted “Figma Make” for turning UIs into prototypes/code.170172 | “Robust but not as advanced”: links, flows, transitions, interactive prototyping; fewer advanced animation/logic features.170171178 |
| Presentation & reviews | Strong presentation/Observation Mode, multi-flow previews, easy sharing.179173 | Real-time multiplayer, comments, shareable prototypes; presentation/review flows less polished.171179 |
Implication: If your workflow leans heavily on complex protos, interactive components, and polished design reviews, Figma is stronger; Penpot is fine for mainstream product flows and user tests.170179171
Collaboration, performance & ecosystem
| Area | Figma | Penpot |
|---|---|---|
| Collaboration model | Best‑in‑class real‑time editing, comments, Observation Mode, integrated FigJam; deep tooling for large teams.169179173 | Real‑time multiplayer, comments, shared links; collaboration solid but less feature‑rich (no full Observation equivalent).171179 |
| Integrations | Broad integrations: Slack, Jira, Trello, Git-based tools, dev H.P.006-WORKFLOWS; third‑party plugin ecosystem plus FigJam.179172173 | Fewer integrations; focus on open formats and dev‑friendly exports (CSS/SVG/HTML) rather than large marketplace.179177 |
| Performance & hosting | Cloud SaaS (plus enterprise options), strong infra but you depend on Figma’s uptime/pricing/policies; large files can feel heavy.169179180 | Web-based and self-hostable with strong performance on large, SVG-heavy and design-system-heavy projects; control reliability yourself.169171177178 |
Implication: For enterprise SaaS with broad toolchains and non‑regulated workloads, Figma’s ecosystem wins; for teams needing self‑hosting, data sovereignty, or close dev alignment, Penpot is compelling.171181177169
Openness, pricing & governance
| Area | Figma | Penpot |
|---|---|---|
| Licensing & cost | Proprietary, per‑seat pricing; free tier with limitations, paid tiers for org features.170173 | Open-source, free; can be self‑hosted or used via hosted offering, cost scales with your infra not per‑seat license.168175177 |
| Data ownership | Data lives in Figma’s cloud (with enterprise agreements for bigger orgs); migration and lock‑in are ongoing concerns for some teams.169181 | Open standards (CSS/SVG/HTML), full data ownership with self‑hosting; no vendor lock‑in by design.168175177 |
| Governance & extensibility | Roadmap controlled by vendor; extensibility via plugins and APIs.169172174 | Community-driven development; extensibility via open codebase and APIs, easier to embed in custom H.P.006-WORKFLOWS.182177178 |
High-level takeaway:
- Choose Figma when you prioritize polished collaboration, advanced prototyping, ecosystem integrations, and AI‑accelerated H.P.006-WORKFLOWS.
- Choose Penpot when you need open source, self‑hosting, strong performance on standards-based design systems, and tighter design–code alignment with full control over data and infra.177175168169170171
⁂