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Agent Skill · 开发

quality-audit(质量审核)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

quality-audit 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 12,549。

总安装 1,234周安装 53GitHub Stars 15下载 432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
MIT

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:quality-audit(质量审核)
来源仓库:https://github.com/nickcrew/claude-ctx-plugin
仓库路径:skills/quality-audit
安装命令:
npx skills add https://github.com/nickcrew/claude-ctx-plugin --skill quality-audit
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/nickcrew/claude-ctx-plugin --skill quality-audit

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

quality-audit 用于辅助安全审计、权限检查、凭据风险与认证流程分析,帮助 Agent 识别潜在漏洞。适用于代码安全复核、依赖风险检查或生成安全审计清单的场景。可调用其审计模块输出检查结果,但不可直接作为最终结论使用。涉及密钥、令牌或生产系统时,应先确认最小权限并确保数据脱敏处理。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/quality-audit

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/nickcrew/claude-ctx-plugin --skill quality-aud…;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

    它需要需要联网,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

quality-audit 怎么安装?

在终端执行 npx skills add https://github.com/nickcrew/claude-ctx-plugin --skill quality-audit。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

quality-audit 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。

quality-audit 需要什么权限?

权限级别是需要联网。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。

quality-audit 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

quality-audit 用的人多吗?

公开数据是累计安装 1,234 次,近一周安装 53 次,下载 432 次,GitHub 15 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。

quality-audit 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
quality-audit

质量审核 · 本页

1,23415需要联网MIT通过

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

Quality Audit Skill

Systematic framework for evaluating skill quality across four dimensions: Clarity, Completeness, Accuracy, and Usefulness.

When to Use This Skill

  • Reviewing a new skill before adding to the registry
  • Auditing existing skills for quality improvements
  • Creating quality rubrics for skill validation
  • Standardizing skill quality across the library
  • Preparing skills for production use

Core Principles

The Four Quality Dimensions

DimensionWeightFocus
Clarity25%Structure, readability, progressive disclosure
Completeness25%Coverage, examples, edge cases, anti-patterns
Accuracy30%Correctness, best practices, security
Usefulness20%Real-world applicability, production-readiness

Scoring Scale (1-5)

ScoreLabelMeaning
1UnacceptableFundamentally broken, dangerous, or unusable
2Needs WorkMajor issues requiring significant revision
3AcceptableMeets minimum standards, functional
4GoodHigh quality, minor improvements possible
5ExcellentExemplary, production-ready, best-in-class

Passing Criteria

  • Minimum: 3.0 weighted average (acceptable)
  • Target: 4.0 weighted average (good)
  • Exceptional: 4.5+ weighted average (excellent)
  • Blocking: Accuracy must be ≥3.0 (no dangerous advice)

Audit Workflow

Phase 1: Structure Check

checklist:
  structure:
    - [ ] Has valid YAML frontmatter
    - [ ] Contains required metadata (name, description)
    - [ ] Follows progressive disclosure (Tier 1 → 2 → 3)
    - [ ] Sections are logically ordered
    - [ ] Token estimate is reasonable (<5000 for core)

Phase 2: Content Evaluation

checklist:
  content:
    - [ ] "When to Use" section is clear
    - [ ] Core principles are well-defined
    - [ ] Code examples are complete and runnable
    - [ ] Anti-patterns are documented
    - [ ] Troubleshooting guidance exists

Phase 3: Dimension Scoring

For each dimension, evaluate against specific criteria:

Clarity Criteria:

  • Well-organized sections with logical flow
  • Concise explanations without jargon overload
  • Code examples are readable and well-commented
  • Progressive disclosure from simple to complex

Completeness Criteria:

  • Covers core concepts thoroughly
  • Includes edge cases and error handling
  • Provides both do's and don'ts
  • Has working examples for main use cases

Accuracy Criteria:

  • Code examples compile/run without errors
  • Follows current best practices (not deprecated)
  • Security considerations are correct
  • Performance claims are verifiable

Usefulness Criteria:

  • Examples solve real-world problems
  • Can be applied immediately
  • Scales to production use cases
  • Includes troubleshooting guidance

Phase 4: Report Generation

## Audit Report: {skill_name}

**Date**: {date}
**Auditor**: {auditor}
**Status**: {PASS|FAIL|NEEDS_REVIEW}

### Scores

| Dimension | Score | Weight | Weighted |
|-----------|-------|--------|----------|
| Clarity | {x}/5 | 25% | {x*0.25} |
| Completeness | {x}/5 | 25% | {x*0.25} |
| Accuracy | {x}/5 | 30% | {x*0.30} |
| Usefulness | {x}/5 | 20% | {x*0.20} |
| **Total** | | | **{sum}/5** |

### Issues Found

- [CRITICAL] {issue description}
- [MAJOR] {issue description}
- [MINOR] {issue description}

### Recommendations

1. {actionable recommendation}
2. {actionable recommendation}

Implementation Patterns

Pattern 1: Quick Audit (5-minute review)

Use for rapid assessment of skill quality:

# Run automated structure checks
cortex skills audit <skill-name> --quick

# Output: Pass/Fail with basic metrics

Quick Audit Checks:

  1. YAML frontmatter valid?
  2. Required sections present?
  3. Code blocks have language tags?
  4. No TODO/FIXME markers?
  5. Token count reasonable?

Pattern 2: Full Audit (15-30 minute review)

Comprehensive evaluation with human review:

# Generate full audit report
cortex skills audit <skill-name> --full

# Interactive mode for scoring
cortex skills audit <skill-name> --interactive

Full Audit Process:

  1. Run automated checks
  2. Read through content manually
  3. Test code examples
  4. Score each dimension
  5. Document issues and recommendations
  6. Generate report

Pattern 3: Comparative Audit

Compare skill against reference implementation:

# Compare against template-skill-enhanced
cortex skills audit <skill-name> --compare template-skill-enhanced

Pattern 4: Batch Audit

Audit multiple skills for registry health:

# Audit all skills in a category
cortex skills audit --category security

# Audit skills below threshold
cortex skills audit --below-score 3.5

CLI Commands

# Basic audit
cortex skills audit <skill-name>

# Options
  --quick           Quick structural check only
  --full            Full audit with all dimensions
  --interactive     Interactive scoring mode
  --output FILE     Write report to file
  --format FORMAT   Output format (markdown|json|yaml)
  --compare SKILL   Compare against reference skill
  --fix             Auto-fix simple issues (formatting)

Creating Custom Rubrics

Skills can define custom rubrics in validation/rubric.yaml:

# validation/rubric.yaml
version: "1.0.0"
skill_name: my-skill

dimensions:
  clarity:
    weight: 25
    criteria:
      - "API examples use realistic data"
      - "Error handling is shown for each operation"
  completeness:
    weight: 25
    criteria:
      - "Covers all HTTP methods"
      - "Includes pagination patterns"
  accuracy:
    weight: 30
    criteria:
      - "Follows REST conventions"
      - "Security headers documented"
  usefulness:
    weight: 20
    criteria:
      - "Examples work with common frameworks"

passing_criteria:
  minimum_score: 3.5  # Higher bar for this skill
  required_dimensions:
    - accuracy
    - completeness

Best Practices

Do

  • Be specific - "Line 45: SQL query vulnerable to injection" not "has security issues"
  • Be actionable - Include how to fix each issue
  • Be fair - Use the same standards consistently
  • Document evidence - Quote specific content for each score
  • Prioritize - Critical issues first, suggestions last

Don't

  • Score based on personal style preferences
  • Mark deprecated patterns without suggesting alternatives
  • Fail skills for missing optional sections
  • Ignore security issues regardless of other scores
  • Rush through audits for complex skills

Anti-Patterns

The Rubber Stamp

Problem: Approving skills without thorough review Why it's bad: Low-quality skills erode trust in the library Fix: Use the full audit checklist, test code examples

The Perfectionist Block

Problem: Failing skills for minor issues Why it's bad: Prevents useful skills from being available Fix: Distinguish between blocking issues and suggestions

Score Inflation

Problem: Giving high scores without justification Why it's bad: Makes scores meaningless Fix: Document specific evidence for each score

Integration with CI/CD

# .github/workflows/skill-quality.yml
name: Skill Quality Gate

on:
  pull_request:
    paths:
      - 'skills/**'

jobs:
  audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install cortex
        run: pip install cortex
      - name: Audit changed skills
        run: |
          for skill in $(git diff --name-only HEAD~1 | grep 'skills/' | cut -d'/' -f2 | uniq); do
            cortex skills audit "$skill" --quick --fail-under 3.0
          done

Troubleshooting

"Audit fails but skill looks fine"

  1. Check YAML frontmatter syntax
  2. Verify all required sections exist
  3. Ensure code blocks have language tags
  4. Check for hidden characters (copy/paste issues)

"Scores seem inconsistent"

  1. Review the scoring guide for each dimension
  2. Calibrate by auditing template-skill-enhanced first
  3. Use --interactive mode for clearer criteria

External Resources

Changelog

1.0.0 (2026-01-05)

  • Initial release
  • Four-dimension scoring framework
  • CLI integration
  • CI/CD workflow example

平台分布

Claude Code

29.05%
按下载量换算125

Antigravity

26.46%
按下载量换算114

Gemini CLI

18.22%
按下载量换算79

OpenCode

11.51%
按下载量换算50

windsurf

7.82%
按下载量换算34

Codex

3.7%
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。

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