Review completed code changes for defects, security risks, unnecessary complexity, and missing tests. Findings are verified and reported with severity, file locations, and concrete failure scenarios, with separate assessments of standards and intended behavior for larger changes.
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Evaluate finished work for defects, risk, and simplification before it ships
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。
Use this skill to evaluate work that already exists. Review never authors the change it is judging.
This is the canonical review workflow. ai-slop-cleaner is an opt-in lane
within it.
Find what is actually wrong, ranked by severity, with enough detail to act on.
A substantial diff is reviewed as two parallel sub-agent passes, reported separately — never merged or cross-ranked, because a change can pass one axis and fail the other (standards-conforming but wrong behavior; faithful but convention-breaking):
docs/standards/ volumes, CONTRIBUTING.md, CODING_STANDARDS.md or equivalents) and review against whatever exists — a standards file you didn't look for is a standard you didn't apply.Keep each sub-agent brief under ~400 words and pointed at one axis. Aggregate without picking a single winner across axes — that reranking is what the separation exists to prevent; state each axis's verdict on its own. A small diff stays one pass.
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Write or revise instructions, specifications, tickets, and skill files so an agent can act without prior chat history. Make rules checkable, explain their reasons, define completion criteria, and keep authoritative information in one place.
中文简介正在准备中。当前显示英语简介。
Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.
中文简介正在准备中。当前显示英语简介。
Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.
中文简介正在准备中。当前显示英语简介。
Send questions, review requests, or implementation tasks to a supported local AI command-line tool through one shared command. The wrapper handles provider-specific options and saves the result as a Markdown file for later reference.
中文简介正在准备中。当前显示英语简介。
Turn an unclear idea into linked decision tickets on a repository’s issue tracker. Work through one decision per session, record outcomes and dependencies, and prepare a brief defining the objective, scope, and exclusions for delivery.
中文简介正在准备中。当前显示英语简介。
Turn a short product idea into working code through requirements analysis, technical planning, implementation, testing, and review. It coordinates agents across these stages, fixes detected problems, and reports assumptions that still need a human decision.
中文简介正在准备中。当前显示英语简介。