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bug-review错误审查

Agent Skill

bug-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

517

周安装

22

GitHub Stars

264

下载量

181
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:bug-review(错误审查)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/bug-review
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill bug-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/athola/claude-night-market --skill bug-review

简介

系统性识别与修复代码缺陷的错误审核工作流程。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中的安全审计场景。
  • 支持特定语言生态的专业知识与静态分析器集成。
  • 安装前需确认权限范围、维护状态及是否修改原始代码。
  • bug-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

Table of Contents

Bug Review Workflow

Systematic bug identification and fixing with language-specific expertise.

Quick Start

/bug-review

Verification: Run the command with --help flag to verify availability.

When To Use

  • Reviewing code for potential bugs
  • After receiving bug reports
  • Before major releases
  • During security audits
  • Investigating production issues

When NOT To Use

  • Test coverage audit - use test-review instead

Required TodoWrite Items

  1. bug-review:language-detected
  2. bug-review:repro-plan
  3. bug-review:defects-documented
  4. bug-review:fixes-prepared
  5. bug-review:verification-plan

Progressive Loading

Load additional context as needed:

  • Language Detection: @include modules/language-detection.md - Manifest heuristics, expertise framing, version constraints
  • Defect Documentation: @include modules/defect-documentation.md - Severity classification, root cause analysis, static analyzers
  • Fix Preparation: @include modules/fix-preparation.md - Minimal patches, idiomatic patterns, test coverage

Workflow

Step 1: Detect Languages (bug-review:language-detected)

Identify dominant languages using manifest files (Cargo.toml → Rust, package.json → Node, etc.).

State expertise persona appropriate for the language ecosystem.

Note version constraints (MSRV, Python versions, Node engines).

Progressive: Load modules/language-detection.md for detailed manifest heuristics.

Step 2: Plan Reproduction (bug-review:repro-plan)

Identify reproduction methods:

  • Unit/integration test suites
  • Fuzzing tools
  • Manual reproduction commands

Document exact commands:

cargo test -p core
pytest tests/test_api.py
npm test -- pkg

Verification: Run pytest -v tests/test_api.py to verify.

Capture blockers and propose mocks when dependencies unavailable.

Step 3: Document Defects (bug-review:defects-documented)

Review code line-by-line, logging each bug with:

  • File:line reference: Precise location
  • Severity: Critical, High, Medium, Low
  • Root cause: Logic error, API misuse, concurrency, resource leak
  • Impact: What breaks and how

Run static analyzers (cargo clippy, ruff check, golangci-lint, eslint).

Use imbue:proof-of-work for reproducible capture.

Progressive: Load modules/defect-documentation.md for classification details and analyzer commands.

Step 4: Prepare Fixes (bug-review:fixes-prepared)

Draft minimal, idiomatic patches using language best practices:

  • Guard clauses (Rust: pattern matching, Python: early returns)
  • Resource cleanup (Go: defer, Python: context managers)
  • Error propagation (Rust:?, Go: wrapped errors)

Create tests following Red → Green pattern:

  1. Write failing test
  2. Apply minimal fix
  3. Verify test passes

Progressive: Load modules/fix-preparation.md for language-specific patterns and test strategies.

Step 5: Verification Plan (bug-review:verification-plan)

Execute reproduction steps with fixes applied.

Capture evidence:

  • Test output logs
  • Benchmark comparisons
  • Coverage reports

Document remaining risks using imbue:diff-analysis/modules/risk-assessment-framework.

Assign owners and deadlines for follow-up items.

Defect Classification (Condensed)

Severity: Critical (crash/data loss) → High (broken features) → Medium (degraded UX) → Low (edge cases)

Root Causes: Logic errors | API misuse | Concurrency issues | Resource leaks | Validation gaps

Output Format

## Summary
[Brief scope description]

## Defects Found
### [D1] file.rs:142 - Title
- Severity: High
- Root Cause: Logic error
- Impact: Data corruption possible
- Fix: [description]

## Proposed Fixes
### Fix for D1
[code diff with explanation]

## Test Updates
[new/updated tests with Red → Green verification]

## Evidence
- Commands executed
- Logs and outputs
- External references

Verification: Run pytest -v to verify tests pass.

Best Practices

  1. Evidence-based: Every finding has file:line reference
  2. Reproducible: Clear steps to reproduce each bug
  3. Minimal fixes: Smallest change that fixes the issue
  4. Test coverage: Every fix has corresponding test
  5. Risk awareness: Document remaining risks with severity scoring

Exit Criteria

  • All defects documented with precise references
  • Fixes prepared with test coverage verified
  • Verification plan includes commands and expected outputs
  • Remaining risks assessed and owners assigned

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

34.24%
按下载量换算62

Claude

30.06%
按下载量换算54

Cursor

19.52%
按下载量换算35

Gemini CLI

8.98%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。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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