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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Validate in any of six modes — spec quality, single-file review, spec-to-implementation drift, constitution rule enforcement, comparison between two artifacts, or sanity-checking your understanding. Use when checking a spec by ID, validating a file by path, detecting drift, enforcing constitution rules, comparing two sources ("$X against $Y"), or asking a freeform validation question.
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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 validation orchestrator that ensures quality and correctness across specifications, implementations, and governance.
Validation Request: $ARGUMENTS
Finding { status: PASS | WARN | FAIL severity: HIGH | MEDIUM | LOW title: string // max 40 chars location: string // file:line issue: string // one sentence recommendation: string // how to fix }
State { target = $ARGUMENTS validationMode: Spec | File | Drift | Constitution | Comparison | Understanding perspectives = [] // from reference/perspectives.md mode: Standard | Agent Team | Dynamic Workflow findings: Finding[] }
Always:
Never:
Determine validation mode from $ARGUMENTS:
match (target) { /^\d{3}/ => Spec Validation file path => File Validation "drift" | "check drift" => Drift Detection "constitution" => Constitution Validation "$X against $Y" => Comparison Validation freeform text => Understanding Validation }
match (mode) { Spec Validation => load spec documents (PRD, SDD, PLAN), identify cross-references Drift Detection => load spec + identify implementation files + extract requirements Constitution => check for CONSTITUTION.md, parse rules by category File Validation => read target file + surrounding context Comparison => load both sources for comparison }
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 validators and collects their findings
Recommend Agent Team when: full spec validation | drift + constitution together | 4+ perspectives | multi-document scope. Recommend Dynamic Workflow instead when those thresholds apply but the perspectives are independent and need no coordination between validators.
Read reference/perspectives.md for the mode-to-perspective mapping.
match (mode) { Standard => launch parallel subagents per applicable perspectives Agent Team => create team, spawn one validator per perspective, assign tasks Dynamic Workflow => run one dynamic workflow: one validator agent per perspective in parallel, returning every finding }
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.
Process findings:
Mode-specific synthesis:
assessment = match (failCount, warnCount) { (0, 0) => Excellent (0, 1..3) => Good (0, > 3) => Needs Attention (> 0, _) => Critical }
Read reference/output-format.md and format the report accordingly.
match (validationMode) { Constitution => ask the user to choose: Apply autofixes (L1) | Show violations | Skip Drift => ask the user to choose: Acknowledge | Update implementation | Update spec | Defer Spec | File => ask the user to choose: Address failures | Show details | Continue anyway }
Called by other workflow skills:
/start:implement — drift check at phase boundaries, constitution check at checkpoints/start:specify — architecture alignment during SDD phase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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