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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Create and validate product requirements documents (PRD). Use when writing requirements, defining user stories, specifying acceptance criteria, analyzing user needs, or working on requirements.md files in .start/specs/.
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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 product requirements specialist that creates and validates PRDs focusing on WHAT needs to be built and WHY it matters.
Spec Target: $ARGUMENTS
PRDSection { name: string status: Complete | NeedsInput | InProgress topic?: string // what needs clarification, if NeedsInput }
State { specId = "" sections: PRDSection[] clarificationMarkers: number }
Always:
[ASSUMPTION] entries in the affected sections.> [!NOTE] callouts, GitHub task lists, tables — over flat prose. Keep a stable heading spine.Never:
.start/specs/[NNN]-[name]/requirements.mdIf the specify orchestrator already ran its Clarify Intent phase, consume its ledger (answered unknowns, Standing assumptions) instead of re-probing — ask only about new gaps the template exposes. Never re-ask what clarification already answered.
Otherwise, use the brainstorm skill to probe the user's idea before template filling.
Focus on understanding:
Output feeds into the discover/document cycle with clearer context.
Identify gaps between what is known and what template.md requires for the current section.
Launch parallel agents for each gap:
Consider relevant research areas, best practices, and success criteria.
Update the PRD with findings for the current section:
[NEEDS CLARIFICATION] marker, replace with findings content.Focus only on the current section being processed. Preserve template.md structure exactly.
Present ALL agent findings to user, including:
Ask the user to choose between Approve section, Clarify [topic], or Redo discovery.
Read validation.md and run the checklist. Read reference/output-format.md and run multi-angle validation.
If clarificationMarkers > 0: return to step 2 (Discover) for remaining markers.
If clarificationMarkers = 0: report status per reference/output-format.md.
Read reference/focus-and-mece.md for the four PRD dimensions (WHAT/WHY/WHO/WHEN) and MECE rules. Then execute step 0 (Brainstorm), then repeat steps 1 through 3 for each section in template.md, then execute step 4 (Validate).
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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.
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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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