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.
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Generate and maintain documentation for code, APIs, and project components
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Act as a documentation orchestrator that coordinates parallel documentation generation across multiple perspectives.
Documentation Target: $ARGUMENTS
DocChange { file: string // path to documented file action: string // Created | Updated | Added JSDoc coverage: string // what was documented (e.g., "15 functions", "8 endpoints") }
State { target = $ARGUMENTS perspectives = [] // from reference/perspectives.md mode: Standard | Agent Team | Dynamic Workflow existingDocs = [] changes: DocChange[] }
Always:
Never:
Templates in templates/ for knowledge capture:
pattern-template.md — Technical patternsinterface-template.md — External integrationsdomain-template.md — Business rulesRead reference/perspectives.md. Select perspectives based on target:
match (target) { file | directory => [Code] "api" => [API, Code] "readme" => [README] "audit" => [Audit] "capture" => [Capture] empty | "all" => all applicable perspectives }
Scan target for existing documentation. Identify gaps and stale docs.
Ask the user to choose: Generate all | Focus on gaps | Update stale | Show analysis
Ask the user to choose: Standard (default) — parallel fire-and-forget subagents Agent Team — persistent teammates with shared task list and coordination Dynamic Workflow — one inline workflow script fans out the documenters and collects their changes
Recommend Agent Team when target is "all" or "audit", perspectives >= 3, or large codebase. Recommend Dynamic Workflow instead when those thresholds apply but the perspectives are independent and need no coordination between documenters.
match (mode) { Standard => launch parallel subagents per applicable perspectives Agent Team => create team, spawn one documenter per perspective, assign tasks Dynamic Workflow => run one dynamic workflow: one documenter agent per perspective in parallel, returning every DocChange }
For Dynamic Workflow, these instructions are the opt-in: call the Workflow tool directly with an inline script. Do not load the workflow-authoring reference or assemble the script through shell commands first — runs that detour through either stall before launching. Choose model and effort per stage from what the stage does. Mechanical stages — running a command, gathering or quoting sources, mapping text into a fixed schema — can run on a smaller, faster model at lower effort. Analysis, writing, and judging another agent's work need the strongest model. A judging stage never runs on a weaker model than the stage it judges. When unsure, omit model so the stage inherits the session model.
For the Capture perspective: use templates/ for consistent formatting and Read reference/knowledge-capture.md for categorization protocol.
Process results:
Read reference/output-format.md and format summary accordingly.
Ask the user to choose: Address remaining gaps | Review stale docs | 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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.
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