Token导航 LogoToken导航

Agent Skill · 研究检索

aif-review(审查)

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。

aif-review 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 50,189。

总安装 624周安装 26GitHub Stars 535下载 208核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/aif-review

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lee-to/ai-factory --skill aif-review;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

aif-review 怎么安装?

在终端执行 npx skills add https://github.com/lee-to/ai-factory --skill aif-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

aif-review 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

aif-review 需要什么权限?

权限级别是调用外部服务。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。

aif-review 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

aif-review 用的人多吗?

公开数据是累计安装 624 次,近一周安装 26 次,下载 208 次,GitHub 535 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

aif-review 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
aif-review

审查 · 本页

624535调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

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

调用外部服务

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills