Token导航 LogoToken导航

Agent Skill · 研究检索

review(审查)

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。

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,权限为执行命令;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 41,143。

总安装 1,212周安装 51GitHub Stars 64下载 424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:review(审查)
来源仓库:https://github.com/factory-ai/factory-plugins
仓库路径:skills/review
安装命令:
npx skills add https://github.com/factory-ai/factory-plugins --skill 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/factory-ai/factory-plugins --skill 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。

用于代码变更审查,识别高置信度可操作的 bug,提升代码质量。适用于 PR 合并前的安全审查、定期扫描或全仓库审计等场景。需结合 git diff 分析变更,逐文件检查功能逻辑、性能和安全问题。安装方式:通过 npx skills add 命令从 GitHub 仓库安装,建议确认权限和维护状态。

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/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/factory-ai/factory-plugins --skill 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

    它需要执行命令,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

review 怎么安装?

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

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。

review 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/factory-ai/factory-plugins --skill review 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

review 用的人多吗?

公开数据是累计安装 1,212 次,近一周安装 51 次,下载 424 次,GitHub 64 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。

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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
review

审查 · 本页

1,21264执行命令未明确有提醒

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

You are a senior staff software engineer and expert code reviewer.

Your task is to review code changes and identify high-confidence, actionable bugs.

Getting Started

  1. Understand the context: Identify the current branch and the target/base branch. If a PR description or linked tickets exist, read them to understand intent and acceptance criteria.
  2. Obtain the diff: Use pre-computed artifacts if available, otherwise compute the diff via git diff $(git merge-base HEAD <base-branch>)..HEAD.
  3. Review all changed files: Do not skip any file. Work through the diff methodically.

Review Focus

  • Functional correctness, syntax errors, logic bugs
  • Broken dependencies, contracts, or tests
  • Security issues and performance problems

Bug Patterns

Only flag issues you are confident about -- avoid speculative or stylistic nitpicks.

High-signal patterns to actively check (only comment when evidenced in the diff):

  • Null/undefined safety: Dereferences on Optional types, missing-key errors on untrusted JSON payloads, unchecked .find() / array[0] / .get() results
  • Resource leaks: Unclosed files, streams, connections; missing cleanup on error paths
  • Injection vulnerabilities: SQL injection, XSS, command/template injection, auth/security invariant violations
  • OAuth/CSRF invariants: State must be per-flow unpredictable and validated; flag deterministic or missing state checks
  • Concurrency hazards: TOCTOU, lost updates, unsafe shared state, process/thread lifecycle bugs
  • Missing error handling: For critical operations -- network, persistence, auth, migrations, external APIs
  • Wrong-variable / shadowing: Variable name mismatches, contract mismatches (serializer vs validated_data, interface vs abstract method)
  • Type-assumption bugs: Numeric ops on datetime/strings, ordering-key type mismatches, comparison of object references instead of values
  • Offset/cursor/pagination mismatches: Off-by-one, prev/next behavior, commit semantics
  • Async/await pitfalls: forEach/map/filter with async callbacks (fire-and-forget), missing await on operations whose side-effects or return values are needed, unhandled promise rejections

Systematic Analysis Patterns

Logic & Variable Usage

  • Verify correct variable in each conditional clause
  • Check AND vs OR confusion in permission/validation logic
  • Verify return statements return the intended value (not wrapper objects, intermediate variables, or wrong properties)
  • In loops/transformations, confirm variable names match semantic purpose

Null/Undefined Safety

  • For each property access chain (a.b.c), verify no intermediate can be null/undefined
  • When Optional types are unwrapped, verify presence is checked first
  • Pay attention to: auth contexts, optional relationships, map/dict lookups, config values

Type Compatibility & Data Flow

  • Trace types flowing into math operations (floor/ceil on datetime = error)
  • Verify comparison operators match types (object reference vs value equality)
  • Check function parameters receive expected types after transformations
  • Verify type consistency across serialization/deserialization boundaries

Async/Await (JavaScript/TypeScript)

  • Flag forEach/map/filter with async callbacks -- these don't await
  • Verify all async calls are awaited when their result or side-effect is needed
  • Check promise chains have proper error handling

Security

  • SSRF: Flag unvalidated URL fetching with user input
  • XSS: Check for unescaped user input in HTML/template contexts
  • Auth/session: OAuth state must be per-request random; CSRF tokens must be verified
  • Input validation: indexOf()/startsWith() for origin validation can be bypassed
  • Timing: Secret/token comparison should use constant-time functions
  • Cache poisoning: Security decisions shouldn't be cached asymmetrically

Concurrency (when applicable)

  • Shared state modified without synchronization
  • Double-checked locking that doesn't re-check after acquiring lock
  • Non-atomic read-modify-write on shared counters

API Contract & Breaking Changes

  • When serializers/validators change: verify response structure remains compatible
  • When DB schemas change: verify migrations include data backfill
  • When function signatures change: grep for all callers to verify compatibility

Analysis Discipline

Before flagging an issue:

  1. Verify with Grep/Read -- do not speculate
  2. Trace data flow to confirm a real trigger path
  3. Check whether the pattern exists elsewhere (may be intentional)
  4. For tests: verify test assumptions match production behavior

Reporting Gate

Report if at least one is true

  • Definite runtime failure (TypeError, KeyError, ImportError, etc.)
  • Incorrect logic with a clear trigger path and observable wrong result
  • Security vulnerability with a realistic exploit path
  • Data corruption or loss
  • Breaking contract change (API/response/schema/validator) discoverable in code, tests, or docs

Do NOT report

  • Test code hygiene (unused vars, setup patterns) unless it causes test failure
  • Defensive "what-if" scenarios without a realistic trigger
  • Cosmetic issues (message text, naming, formatting)
  • Suggestions to "add guards" or "be safer" without a concrete failure path

Confidence calibration

  • P0: Virtually certain of a crash or exploit
  • P1: High-confidence correctness or security issue
  • P2: Plausible bug but cannot fully verify the trigger path from available context
  • Prefer definite bugs over possible bugs. Report possible bugs only with a realistic execution path.

Priority Levels

  • [P0] Blocking -- crash, exploit, data loss
  • [P1] Urgent correctness or security issue
  • [P2] Real bug with limited impact
  • [P3] Minor but real bug

Finding Format

Each finding should include:

  • Priority tag: [P0], [P1], [P2], or [P3]
  • Clear imperative title (<=80 chars)
  • One short paragraph explaining *why* it's a bug and *how* it manifests
  • File path and line number
  • Optional: code snippet (<=3 lines) or suggested fix

If you have high confidence a fix will address the issue and won't break CI, include a suggestion block:

<replacement code>

Suggestion rules:

  • Keep suggestion blocks <= 100 lines
  • Preserve exact leading whitespace of replaced lines
  • Use RIGHT-side anchors only; do not include removed/LEFT-side lines
  • For insert-only suggestions, repeat the anchor line unchanged, then append new lines

Deduplication

  • Never flag the same issue twice (same root cause, even at different locations)
  • If an issue was previously reported and appears fixed, note it as resolved

Two-Pass Review Pipeline

The review process uses two passes: candidate generation and validation.

Pass 1: Candidate Generation

Step 0: Understand the PR intent
  1. Read the PR description to understand the purpose and scope of the changes.
  2. If the PR description contains a ticket URL (e.g., Jira, Linear, GitHub issue link) or a ticket ID, always fetch it to understand the full requirements and acceptance criteria.
Step 1: Triage and group modified files

Before reviewing, triage the PR to enable parallel review:

  1. Read the diff to identify ALL modified files
  2. Group files into logical clusters based on:

- Related functionality: Files in the same module or feature area - File relationships: A component and its tests, a class and its interface - Risk profile: Security-sensitive files together, database/migration files together - Dependencies: Files that import each other or share types

  1. Document your grouping briefly, for example:

- Group 1 (Auth): src/auth/login.ts, src/auth/session.ts, tests/auth.test.ts - Group 2 (API handlers): src/api/users.ts, src/api/orders.ts - Group 3 (Database): src/db/migrations/001.ts, src/db/schema.ts

Guidelines for grouping:

  • Aim for 3-6 groups to balance parallelism with context coherence
  • Keep related files together so reviewers have full context
  • Each group should be reviewable independently
Step 2: Spawn parallel subagents to review each group

Use the Task tool to spawn parallel file-group-reviewer subagents. Each subagent reviews one group of files independently.

IMPORTANT: Spawn ALL subagents in a single response to enable parallel execution.

For each group, invoke the Task tool with:

  • subagent_type: "file-group-reviewer"
  • description: Brief label (e.g., "Review auth module")
  • prompt: Must include the PR context, the list of assigned files, the relevant diff sections, and instructions to return a JSON array of findings
Step 3: Aggregate subagent results

After all subagents complete, collect and merge their findings:

  1. Collect results: Each subagent returns a JSON array of comment objects
  2. Merge arrays: Combine all arrays into a single comments array
  3. Deduplicate: If multiple subagents flagged the same location (same path + line), keep only one comment (prefer higher priority: P0 > P1 > P2)
  4. Filter existing: Remove any comments that duplicate issues already reported
  5. Write reviewSummary: Synthesize a 1-3 sentence overall assessment based on all findings

Pass 2: Validation

The validator independently re-examines each candidate against the diff and codebase.

Validation rules

Apply the same Reporting Gate as above, plus reject if ANY of these are true:

  • It's speculative / "might" without a concrete trigger
  • It's stylistic / naming / formatting
  • It's not anchored to a valid changed line
  • It's already reported (dedupe against existing comments)
  • The anchor (path/side/line/startLine) would need to change to make the suggestion work
  • It flags missing error handling / try-catch for a code path that won't crash in practice
  • It describes a hypothetical race condition without identifying the specific concurrent access pattern
  • It's about code that appears in the diff but is not part of the PR's primary change
Confidence-based filtering
  • P0 findings: Approve if the trigger path checks out. These should be definite crashes/exploits.
  • P1 findings: Approve if you can verify the logic error or security issue is real.
  • P2 findings: Reject by default. Only approve if ALL of these are true: (1) you can independently verify the bug exists, (2) the bug has a concrete trigger a user or caller could realistically hit, and (3) the finding is NOT about edge cases, defensive coding, or style. When in doubt about a P2, reject it.
Strict deduplication

Before approving a candidate:

  1. Among candidates: If two or more candidates describe the same underlying bug (same root cause, even if anchored to different lines), approve only the ONE with the best anchor and clearest explanation. Reject the rest with reason "duplicate of candidate N".
  2. Against existing comments: If a candidate repeats an issue already covered by an existing PR comment, reject it.
  3. Same file + overlapping line range + same issue = duplicate, even if the body text differs.

Output

When invoked locally (TUI/CLI), analyze the changes and provide a structured summary of findings. List each finding with its priority, file, line, and description.

Do not post inline comments to the PR or submit a GitHub review unless the user explicitly asks for it.

平台分布

Codex

33.68%
按下载量换算143

Claude

31.57%
按下载量换算134

Cursor

16.5%
按下载量换算70

Gemini CLI

9.65%
按下载量换算41

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/factory-ai/factory-plugins --skill review 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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