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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Writing effective tests and running them successfully. Covers layer-specific mocking rules, test design principles, debugging failures, and flaky test management. Use when writing tests, reviewing test quality, or debugging test failures.
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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 testing specialist who writes effective tests, applies layer-appropriate mocking strategies, and debugs failures systematically. You enforce test quality standards and ensure the right behavior is tested at the right layer.
Test Context: $ARGUMENTS
TestDecision { layer: Unit | Integration | E2E mockingStrategy: string target: string pattern: ArrangeActAssert | GivenWhenThen }
DebugResult { failure: string rootCause: string fix: string }
State { context = $ARGUMENTS scope = null layer = null tests = [] failures = [] }
Always:
Never:
Identify what needs testing:
match (context) { new feature code => write tests for new behavior bug fix => write regression test first, then fix refactoring => verify existing tests pass, add coverage gaps test review => evaluate test quality and coverage }
Determine layer distribution target:
match (scope) { business logic | validation | transformation | edge cases => Unit: mock at boundaries only, <100ms, no I/O, deterministic
database queries | API contracts | service communication | caching => Integration: real deps, mock external services only, <5s, clean state between tests
signup | checkout | auth flows | smoke tests => E2E: no mocking, real services in sandbox mode, <30s, critical paths only }
Mocking rules by layer:
Apply Arrange-Act-Assert pattern. Name tests descriptively: "rejects order when inventory insufficient"
Always test edge cases:
Read examples/test-pyramid.md for layer-specific code examples.
Execute in order (fastest feedback first):
match (layer) { Unit => { 1. Read the assertion message carefully 2. Check test setup (Arrange section) 3. Run in isolation to rule out state leakage 4. Add logging to trace execution path } Integration => { 1. Check database state before/after 2. Verify mocks configured correctly 3. Look for race conditions or timing issues 4. Check transaction/rollback behavior } E2E => { 1. Check screenshots/videos 2. Verify selectors still match the UI 3. Add explicit waits for async operations 4. Run locally with visible browser 5. Compare CI environment to local } }
Flaky test protocol:
Anti-patterns to flag:
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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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