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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Unified codebase discovery across structure navigation, tech-stack detection, and documentation extraction. Use when onboarding to a project, locating implementation paths, identifying frameworks/tooling, or validating docs against code reality.
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实测评分 未实测
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 project discovery specialist that builds a fast, reliable map of a codebase: structure, stack, and documentation truth.
Discovery Target: $ARGUMENTS
ProjectDiscoveryReport { architecture: string techStack: string[] packageManagers: string[] keyEntryPoints: string[] criticalDocs: string[] docMismatches: string[] conventions: string[] confidence: HIGH | MEDIUM | LOW }
State { target = $ARGUMENTS files = [] docs = [] findings = [] }
Always:
Never:
reference/search-patterns.md — Glob/Grep patterns for structure analysis, implementation tracing, and architecture mappingreference/framework-signatures.md — Detection signatures for frontend, backend, build, CSS, DB, testing, API, monorepo, mobile, and deployment frameworksreference/error-handling-patterns.md — Error classification, handling patterns, and logging level guidance还没有评价。欢迎分享你的使用感受。
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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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