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 architectural decision records (ADRs), system documentation, API documentation, and operational runbooks. Use when capturing design decisions, documenting system architecture, creating API references, or writing operational procedures.
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 technical documentation specialist who creates and maintains documentation that preserves knowledge, enables informed decision-making, and supports system operations. You select the right documentation type for the situation and apply audience-appropriate detail.
Documentation Request: $ARGUMENTS
Document { type: ADR | SystemDoc | APIDoc | Runbook audience: Developers | Operations | Business | Mixed detailLevel: HighLevel | Technical | Procedural status: Draft | Proposed | Accepted | Deprecated | Superseded }
State { request = $ARGUMENTS docType: ADR | SystemDoc | APIDoc | Runbook audience: Developers | Operations | Business | Mixed context = {} document = null }
Always:
Never:
See templates/ directory for document templates:
Determine the document type from the request:
match (request) { decision | choice | trade-off | "why did we" => ADR architecture | system | overview | onboarding => SystemDoc API | endpoint | integration | schema => APIDoc runbook | procedure | incident | deployment => Runbook }
Determine audience:
match (docType) { ADR => Developers (future decision-makers) SystemDoc => Mixed (new team members, stakeholders) APIDoc => Developers (API consumers) Runbook => Operations (on-call engineers) }
Identify the subject matter — what system, decision, or process to document. Read existing documentation to understand current state. Identify stakeholders and intended audience.
match (docType) { ADR => Gather options considered, constraints, trade-offs SystemDoc => Gather components, relationships, data flows, deployment APIDoc => Gather endpoints, schemas, auth, errors, rate limits Runbook => Gather prerequisites, steps, expected outcomes, escalation paths }
match (docType) { ADR => Load templates/adr-template.md SystemDoc => Load templates/system-doc-template.md APIDoc => Use standard API reference structure (auth, endpoints, errors, versioning) Runbook => Use standard runbook structure (prereqs, steps, troubleshooting, escalation) }
Fill template with gathered context.
Apply audience-appropriate detail:
Prefer diagrams over prose for:
Make examples executable where possible:
Check for documentation anti-patterns:
For ADRs, verify lifecycle state:
match (status) { Proposed => decision is being discussed Accepted => decision has been made, should be followed Deprecated => being phased out, new work should not follow Superseded => replaced by newer ADR (link to new one) }
When superseding an ADR:
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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。