Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Create or update a project constitution with governance rules. Uses discovery-based approach to generate project-specific rules.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Act as a governance orchestrator that coordinates parallel pattern discovery to create project constitutions.
Focus Areas: $ARGUMENTS
Rule { level: L1 | L2 | L3 // Must (autofix) | Should (manual) | May (advisory) category: string // Security, Architecture, CodeQuality, Testing, or custom statement: string // the rule itself evidence: string // file:line references supporting the rule }
State { focusAreas = $ARGUMENTS perspectives = [] // from reference/perspectives.md existing: boolean discoveries: Rule[] }
Always:
match (CONSTITUTION.md at project root) { exists => read and parse existing rules, route to update flow not found => read template.md, route to creation flow }
Read reference/perspectives.md. Select applicable perspectives based on $ARGUMENTS.
Launch parallel agents for each perspective. Each agent explores the codebase and returns proposed Rules with evidence.
Process discoveries:
Read reference/output-format.md and format proposed rules accordingly.
Ask the user to choose: Approve rules | Modify before saving | Cancel
match (existing) { true => merge approved rules into existing CONSTITUTION.md false => write new CONSTITUTION.md from template + approved rules }
Display constitution summary per reference/output-format.md.
Ask the user to choose: Run validation now | Skip
match (choice) { validate => use the validate skill in constitution mode skip => done }
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Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Deep-research analysis of a codebase, a topic, or both. Clarifies the question, agrees the research angles with you, then runs a dynamic workflow that gathers cited claims per angle, merges duplicates, has independent skeptics try to refute every claim, and writes one cited report from the claims that survived. Use for "how does X work", "map the Y flow", "research Z", or any mechanism-level question that must be evidence-backed.
日本語の概要は準備中です。原文の説明を表示しています。
REST and GraphQL API design patterns, OpenAPI/Swagger specifications, versioning strategies, and authentication patterns. Use when designing APIs, reviewing API contracts, evaluating API technologies, or implementing API endpoints.
日本語の概要は準備中です。原文の説明を表示しています。
System architecture patterns including monolith, microservices, event-driven, and serverless, with C4 modeling, scalability strategies, and technology selection criteria. Use when designing system architectures, evaluating patterns, or planning scalability.
日本語の概要は準備中です。原文の説明を表示しています。
Consolidate a completed specification into a single archived record and remove its working directory. Use as the final stage after implementation — distills requirements + solution into .start/archive/[NNN]-[name].md and deletes the .start/specs/[NNN]-[name]/ directory.
日本語の概要は準備中です。原文の説明を表示しています。
Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.
日本語の概要は準備中です。原文の説明を表示しています。