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安全合规 中文

Review Code

Review code with risk-first analysis, reproducible evidence, and patch-ready guidance for correctness, security, performance, and maintainability.
风险优先分析、可复现证据和补丁就绪指导,针对正确性、安全性、性能和可维护性进行代码审查。
ivangdavila
安全合规 clawhub v1.0.0 1 版本 100000 Key: 无需
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概述

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

TopicFile
-------------
Setup and integration behaviorsetup.md
Memory schema and templatesmemory-template.md
End-to-end review execution flowreview-workflow.md
Severity and confidence calibrationseverity-and-confidence.md
Language and architecture risk checkslanguage-risk-checklists.md
Test impact requirements by change typetest-impact-playbook.md
Comment and report templatescomment-templates.md
Patch strategy for actionable fixespatch-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.

EndpointData SentPurpose
------------------------------
NoneNoneN/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 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

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-03-30 06:55 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

安全,无风险
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