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Review Code

作者 Iván · GitHub ↗ · v1.0.0
darwinlinuxwin32 ✓ 安全检测通过
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在 OpenClaw 中安装
/install review-code
功能描述
Review code with risk-first analysis, reproducible evidence, and patch-ready guidance for correctness, security, performance, and maintainability.
使用说明 (SKILL.md)

Setup

On first use, read setup.md for integration guidance and local memory initialization.

When to Use

User asks for a code review, PR review, merge-readiness check, or bug-risk audit before shipping. Agent delivers a risk-ranked review with explicit evidence, impact, confidence, and concrete fix direction.

Architecture

Memory lives in ~/review-code/. See memory-template.md for structure and starter templates.

~/review-code/
├── memory.md             # Review preferences, stack context, and recent constraints
├── findings/             # Optional per-review finding logs
├── baselines/            # Team conventions and accepted risk baselines
└── sessions/             # Session summaries for ongoing audits

Quick Reference

Topic File
Setup and integration behavior setup.md
Memory schema and templates memory-template.md
End-to-end review execution flow review-workflow.md
Severity and confidence calibration severity-and-confidence.md
Language and architecture risk checks language-risk-checklists.md
Test impact requirements by change type test-impact-playbook.md
Comment and report templates comment-templates.md
Patch strategy for actionable fixes patch-strategy.md

Data Storage

Local notes stay in ~/review-code/. Before creating or changing local files, present the planned write and ask for user confirmation.

Core Rules

1. Define the Review Contract First

Confirm target scope before reviewing: branch, files, risk tolerance, and release context. If scope is unclear, state assumptions explicitly and keep findings tied to those assumptions.

2. Start With Risk Mapping, Then Deep Dive

Run a fast pass to locate high-risk zones first: auth, money, data integrity, concurrency, and migration paths. Only then perform line-level analysis with review-workflow.md so major failures are surfaced early.

3. Every Finding Must Be Evidence-Backed

Do not report vague concerns. Each finding must include: trigger location, concrete failure mode, user or business impact, and minimal reproduction clue. If evidence is weak, mark low confidence or downgrade to a question.

4. Separate Blocking vs Advisory With Severity + Confidence

Use severity-and-confidence.md for consistent triage. Blocking findings must be reproducible or highly probable with strong impact. Advisory feedback must remain concise and never hide blockers.

5. Always Pair Findings With a Fix Path

For each blocking issue, provide a minimally disruptive fix strategy. Use patch-strategy.md to propose rollback-safe edits, guard tests, and verification steps.

6. Tie Review Quality to Test Impact

Map each change to required tests using test-impact-playbook.md. If tests are missing, list the exact scenarios that must be added and why they prevent regressions.

7. Optimize for Signal, Not Volume

Prioritize high-impact defects over style noise. If no blockers are found, state that explicitly and list residual risks, test gaps, and monitoring advice.

Common Traps

  • Reporting opinions as facts -> review credibility drops and teams ignore real blockers.
  • Mixing blocker and nit feedback without labels -> delayed merges and mis-prioritized fixes.
  • Calling something “probably fine” without tests -> silent regressions in production.
  • Suggesting large rewrites for local defects -> good fixes are postponed indefinitely.
  • Ignoring release context (hotfix vs refactor) -> wrong trade-offs for urgency.
  • Missing migration and backward-compatibility checks -> runtime failures after deploy.

External Endpoints

This skill makes NO external network requests.

Endpoint Data Sent Purpose
None None N/A

No other data is sent externally.

Security & Privacy

Data that leaves your machine:

  • Nothing by default. This is an instruction-only review skill unless the user explicitly exports artifacts.

Data stored locally:

  • Review preferences, project constraints, and optional findings approved by the user.
  • Stored in ~/review-code/.

This skill does NOT:

  • auto-approve code or merge pull requests.
  • make undeclared network calls.
  • store credentials, tokens, or sensitive payloads.
  • modify its own core instructions or auxiliary files.

Trust

This is an instruction-only code review skill. No credentials are required and no third-party services are contacted by default.

Related Skills

Install with clawhub install \x3Cslug> if user confirms:

  • code - implementation workflow that complements review findings.
  • git - safer branch, diff, and commit handling during remediation.
  • typescript - stricter typing and runtime safety review for TS-heavy codebases.
  • ci-cd - release-gate checks and deployment safeguards after fixes.
  • devops - production risk assessment and rollback planning.

Feedback

  • If useful: clawhub star review-code
  • Stay updated: clawhub sync
安全使用建议
This skill is instruction-only and appears to do what it says: risk-focused code reviews using local templates and optional local storage in ~/review-code/. Before enabling or running it: (1) confirm you are comfortable with the agent creating files under ~/review-code/ (the skill promises to ask before writing), (2) do not store secrets or credentials in the review memory, and (3) if you prefer ephemeral reviews, decline setup or remove ~/review-code/ after use. If you later install the related skills (code, git, ci-cd, etc.), review their permissions separately because those may request additional access.
功能分析
Type: OpenClaw Skill Name: review-code Version: 1.0.0 The 'Review Code' skill bundle consists of structured Markdown instructions and templates designed to guide an AI agent through a professional code review workflow. It emphasizes risk-first analysis, evidence-backed findings, and local data storage in '~/review-code/', with explicit instructions to avoid storing secrets or making unauthorized network calls (SKILL.md, setup.md). No malicious code, data exfiltration triggers, or harmful prompt injections were identified.
能力评估
Purpose & Capability
Name and description (risk-focused code review) align with the contents: checklists, templates, workflow, and setup. The skill requests no binaries, credentials, or external services that would be unrelated to a code review.
Instruction Scope
SKILL.md and the companion documents strictly describe review behavior, risk checklists, templates, and local note storage. Instructions do not direct the agent to read unrelated system files or environment variables, nor to call external endpoints. The skill explicitly requires user confirmation before creating or changing local files.
Install Mechanism
There is no install spec and no code to execute; the skill is instruction-only. That minimizes disk writes and execution risk. The 'related skills' list is advisory; installing them would be a separate user action.
Credentials
The skill requires no environment variables, no credentials, and no config paths beyond an optional directory under the user's home. This is proportionate to a local code-review instruction set. The docs explicitly advise not to store secrets.
Persistence & Privilege
The skill recommends using ~/review-code/ for local memory and findings. This is reasonable for a review workflow but does create persistent local files if the user consents. The instructions state the agent must present planned writes and ask for confirmation before creating/changing files.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install review-code
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /review-code 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release with risk-first review workflow, severity-confidence scoring, and fix-ready output templates.
元数据
Slug review-code
版本 1.0.0
许可证
累计安装 4
当前安装数 4
历史版本数 1
常见问题

Review Code 是什么?

Review code with risk-first analysis, reproducible evidence, and patch-ready guidance for correctness, security, performance, and maintainability. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 511 次。

如何安装 Review Code?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install review-code」即可一键安装,无需额外配置。

Review Code 是免费的吗?

是的,Review Code 完全免费(开源免费),可自由下载、安装和使用。

Review Code 支持哪些平台?

Review Code 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux, win32)。

谁开发了 Review Code?

由 Iván(@ivangdavila)开发并维护,当前版本 v1.0.0。

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