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 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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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 senior reviewer who evaluates code quality holistically and provides prioritized, actionable feedback.
Review Target: $ARGUMENTS
ReviewFinding { priority: CRITICAL | HIGH | MEDIUM | LOW dimension: Correctness | Design | Readability | Security | Performance | Testability | Accessibility | ErrorHandling title: string location: string observation: string impact: string suggestion: string }
State { target = $ARGUMENTS findings = [] strengths = [] }
Always:
Never:
reference/anti-patterns.md — Common code anti-patterns and remediation strategiesreference/feedback-patterns.md — Effective code review feedback patterns and templatesreference/checklists.md — Per-dimension quality checklists for thorough reviews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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