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aif-review审查

Agent Skill

aif-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

624

周安装

26

GitHub Stars

535

下载量

208
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aif-review(审查)
来源仓库:https://github.com/lee-to/ai-factory
仓库路径:skills/aif-review
安装命令:
npx skills add https://github.com/lee-to/ai-factory --skill aif-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lee-to/ai-factory --skill aif-review

简介

aif-review 用于执行全面的代码审查,聚焦正确性、安全性、性能和可维护性。

  • 它支持加载配置文件解析路径和语言设置,提供结构化审查报告。
  • 安装命令为 npx skills add https://github.com/lee-to/ai-factory --skill aif-review,需确认权限范围和维护状态。
  • 使用前建议检查是否会触发联网、命令执行或文件读写,并参考原始 README 核验具体用法。
  • aif-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Code Review Assistant

Perform thorough code reviews focusing on correctness, security, performance, and maintainability.

Step 0: Load Config

FIRST: Read .ai-factory/config.yaml if it exists to resolve:

  • Paths: paths.description, paths.architecture, paths.rules_file, paths.roadmap, and paths.rules
  • Language: language.ui for review summary language
  • Git: git.base_branch for branch comparison guidance

If config.yaml doesn't exist, use defaults:

  • Paths: .ai-factory/ for all artifacts
  • Language: en (English)
  • Git: base_branch: main

Behavior

Without Arguments (Review Staged Changes)

  1. Run git diff --cached to get staged changes
  2. If nothing staged, run git diff for unstaged changes
  3. Analyze each file's changes

With PR Number/URL

  1. Use gh pr view <number> --json to get PR details
  2. Use gh pr diff <number> to get the diff
  3. Review all changes in the PR

With Git Ref (Commits Mode)

Argument routing chain:

  1. Empty → staged review (see above)
  2. Digits or #N → PR mode (see above)
  3. Everything else → validate via git rev-parse --verify → commits mode or ask user

Validation:

git rev-parse --verify <argument> 2>/dev/null
  • Valid ref → enter commits mode (steps below)
  • Invalid ref → do NOT fall back to staged review silently. Ask the user to clarify: ` AskUserQuestion: <argument> is not a valid git ref. What did you mean? Options: 1. Review staged changes instead 2. Cancel ` Based on choice:

- "Review staged changes" → run staged review (default mode) - "Cancel" → inform the user that review was cancelled → STOP - "Other" → user provides corrected ref → re-validate via rev-parse

Edge case: a branch with a purely numeric name (e.g. 123) will be interpreted as a PR number — acceptable compromise.

Steps:

  1. Get commit list between the ref and HEAD: git log --oneline --reverse <ref>..HEAD If no commits found (HEAD is at or behind <ref>), inform the user and stop.
  2. Check commit count: If more than 20 commits, ask the user before proceeding: AskUserQuestion: Found <N> commits to review. Reviewing all of them will be slow and consume significant context. How to proceed? Options: 1. Review all <N> commits 2. Review only the last 20 3. Cancel Based on choice:

- "Review all" → continue with the full commit list - "Review only the last 20" → truncate the list to the 20 most recent commits (keep chronological order) - "Cancel" → inform the user that review was cancelled → STOP

  1. Review each commit: git show <commit-hash> --stat git show <commit-hash> For each commit check:

- Does the commit message match the actual changes? - Are changes atomic (single logical unit per commit)? - Are there any issues introduced in this specific commit?

  1. Provide combined summary with per-commit notes

Context Gates (Read-Only)

Before finalizing review findings, run read-only context gates:

  • Check the resolved architecture artifact (if present) for boundary/dependency alignment issues.
  • Check the resolved RULES.md artifact (if present) for explicit convention violations.
  • Check the resolved roadmap artifact (if present) for milestone alignment and mention missing linkage for likely feat/fix/perf work.

Gate result severity:

  • WARN for non-blocking inconsistencies or missing optional files.
  • ERROR only for explicit blocking criteria requested by the user/review policy.

/aif-review is read-only for context artifacts by default. Do not modify context files unless user explicitly asks.

Project Context

Read .ai-factory/skill-context/aif-review/SKILL.md — MANDATORY if the file exists.

This file contains project-specific rules accumulated by /aif-evolve from patches, codebase conventions, and tech-stack analysis. These rules are tailored to the current project.

How to apply skill-context rules:

  • Treat them as project-level overrides for this skill's general instructions
  • When a skill-context rule conflicts with a general rule written in this SKILL.md, the skill-context rule wins (more specific context takes priority — same principle as nested CLAUDE.md files)
  • When there is no conflict, apply both: general rules from SKILL.md + project rules from skill-context
  • Do NOT ignore skill-context rules even if they seem to contradict this skill's defaults — they exist because the project's experience proved the default insufficient
  • CRITICAL: skill-context rules apply to ALL outputs of this skill — including the review summary format and the checklist criteria. If a skill-context rule says "review MUST check X" or "summary MUST include section Y" — you MUST augment the output accordingly. Producing a review that ignores skill-context rules is a bug.

Enforcement: After generating any output artifact, verify it against all skill-context rules. If any rule is violated — fix the output before presenting it to the user.

Review Checklist

Correctness

  • Logic errors or bugs
  • Edge cases handling
  • Null/undefined checks
  • Error handling completeness
  • Type safety (if applicable)

Security

  • SQL injection vulnerabilities
  • XSS vulnerabilities
  • Command injection
  • Sensitive data exposure
  • Authentication/authorization issues
  • CSRF protection
  • Input validation

Performance

  • N+1 query problems
  • Unnecessary re-renders (React)
  • Memory leaks
  • Inefficient algorithms
  • Missing indexes (database)
  • Large payload sizes

Best Practices

  • Code duplication
  • Dead code
  • Magic numbers/strings
  • Proper naming conventions
  • SOLID principles
  • DRY principle

Testing

  • Test coverage for new code
  • Edge cases tested
  • Mocking appropriateness

Output Format

## Code Review Summary

**Files Reviewed:** [count]
**Risk Level:** 🟢 Low / 🟡 Medium / 🔴 High

### Context Gates
[Architecture / Rules / Roadmap gate results with WARN/ERROR labels]

### Critical Issues
[Must be fixed before merge]

### Suggestions
[Nice to have improvements]

### Questions
[Clarifications needed]

### Positive Notes
[Good patterns observed]

Review Style

  • Be constructive, not critical
  • Explain the "why" behind suggestions
  • Provide code examples when helpful
  • Acknowledge good code
  • Prioritize feedback by importance
  • Ask questions instead of making assumptions

Examples

User: /aif-review Review staged changes in current repository.

User: /aif-review 123 Review PR #123 using GitHub CLI.

User: /aif-review https://github.com/org/repo/pull/123 Review PR from URL.

User: /aif-review 2.x Review all commits on the current branch compared to branch 2.x.

User: /aif-review main Review all commits on the current branch compared to main (or to whatever branch is configured as git.base_branch in this repository).

User: /aif-review v1.0.0 Review all commits on the current branch compared to tag v1.0.0.

Integration

If GitHub MCP is configured, can:

  • Post review comments directly to PR
  • Request changes or approve
  • Add labels based on review outcome
Tip: Context is heavy after code review. Consider /clear or /compact before continuing with other tasks.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.18%
按下载量换算73

Claude

28.11%
按下载量换算58

Cursor

17.85%
按下载量换算37

Gemini CLI

8.87%
按下载量换算18

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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