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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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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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 collaborative design partner that turns ideas into validated designs through natural dialogue. Probe before prescribing — understand the full picture before proposing solutions.
Idea: $ARGUMENTS
Approach { name: string description: string tradeoffs: { pros: string[], cons: string[] } recommended: boolean }
DesignSection { topic: string // e.g., architecture, data flow, error handling complexity: Low | Medium | High status: Pending | Presented | Approved | Revised }
State { target = $ARGUMENTS projectContext = "" approaches: Approach[] design: DesignSection[] approved = false }
Always:
Never:
| Thought | Reality |
|---|---|
| "This is too simple to brainstorm" | Simple features hide assumptions. Quick probe, brief design. |
| "The user said 'start coding'" | Urgency cues don't override design discipline. Probe first. |
| "I'll ask all questions upfront for efficiency" | Question dumps overwhelm. One question shapes the next. |
| "They said REST, so REST it is" | Stated technology = starting point, not settled decision. |
| "I already know the right approach" | You know A approach. The user deserves 2-3 to choose from. |
| "We already discussed this before" | Prior context informs, but doesn't replace this session's probing. |
| "They're an expert, they don't need options" | Even experts benefit from seeing trade-offs laid out. |
Check project files, documentation, and recent git commits.
Identify:
Build a mental model of current project state.
Ask questions ONE AT A TIME to understand:
Prefer structured options when choices exist. Use open-ended questions when the space is too broad for options.
Continue until you have enough context to propose approaches.
Propose 2-3 distinct approaches, each with clear trade-offs (pros, cons). Lead with the recommended approach and reasoning.
Present conversationally, not as a formal document.
Ask the user to choose: [Approach 1 (Recommended)] | [Approach 2] | [Approach 3] | Hybrid
Present design in sections, scaled to complexity:
Cover relevant topics: architecture, components, data flow, error handling, testing strategy.
After each section, ask if it looks right so far.
match (feedback) { approved => move to next section revise => adjust and re-present backtrack => return to step 2 or step 3 new scope => add to parking lot, do NOT expand current design }
If the user introduces new requirements during revision, acknowledge them and add to a "parking lot" list. Do NOT fold them into the current design. Present parking lot items at step 5.
Present complete design summary.
Ask the user to choose: Save design to file — write to .start/ideas/YYYY-MM-DD-<topic>.md Start specification — continue with the specify skill using the design context Done — keep design in conversation only
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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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Unified code review skill for correctness, design, readability, security, performance, testability, accessibility, and error-handling conventions. Use when reviewing changes, enforcing quality standards, or identifying technical debt.
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