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Agent Skill · 研究检索

deep-audit(深度审计)

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。

deep-audit 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 61,288。

总安装 353周安装 15GitHub Stars 1,012下载 124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:deep-audit(深度审计)
来源仓库:https://github.com/pedrohcgs/claude-code-my-workflow
仓库路径:skills/deep-audit
安装命令:
npx skills add https://github.com/pedrohcgs/claude-code-my-workflow --skill deep-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。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/pedrohcgs/claude-code-my-workflow --skill deep-audit

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, 适合梳理敏感配置或生成安全复核清单。deep-audit 属于研究检索类 Skill,可作为该场景下的辅助能力补充。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。

安装方式:通过 npx skills add 命令从指定 GitHub 仓库添加。

来源仓库内路径:skills/deep-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/pedrohcgs/claude-code-my-workflow --skill deep…;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

deep-audit 怎么安装?

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

deep-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。

deep-audit 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/pedrohcgs/claude-code-my-workflow --skill deep-audit 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。

deep-audit 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。

deep-audit 用的人多吗?

公开数据是累计安装 353 次,近一周安装 15 次,下载 124 次,GitHub 1,012 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。

deep-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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 1,012 星,维护活跃度相对可查。

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
deep-audit

深度审计 · 本页

3531,012执行命令未明确通过

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

/deep-audit — Repository Infrastructure Audit

Run a comprehensive consistency audit across the entire repository, fix all issues found, and loop until clean.

When to Use

  • After broad changes (new skills, rules, hooks, guide edits)
  • Before releases or major commits
  • When the user asks to "find inconsistencies", "audit", or "check everything"

Workflow

PHASE 0: Mechanical checks (run FIRST, cheap, deterministic)

Before spawning agents, run the mechanical parity checks:

python3 scripts/check-skill-integrity.py --verbose

This catches four classes of bug that agent-based audits have historically missed:

  1. Frontmatter allowed-tools ↔ body tool-invocation parity (e.g. body spawns Task but Task not in allowed-tools — the v1.7.0 PR #92 miss).
  2. argument-hint ↔ body flag parity (flags documented but not advertised, or vice versa).
  3. Internal markdown anchors resolve (no broken [text](path#anchor) links — the #category-11-numerical-discipline miss on PR #87).
  4. Rule paths: ↔ skill implementation parity (rule claims skill follows protocol but skill body has none of the protocol keywords — the /interview-me miss on PR #92).

If Phase 0 reports P0 or P1 findings, fix them (or tune the regex if they are false positives) before launching the 4 agents. The mechanical layer is cheaper and more precise than agent prompts for these classes.

PHASE 1: Launch 4 Parallel Audit Agents

Launch these 4 agents simultaneously using Task with subagent_type=general-purpose. Each agent's prompt must tell it to read .claude/references/audit-pet-peeves.md and explicitly check for each class of bug before reporting clean. The pet-peeves file is a living catalogue of drift patterns review bots have caught; it grows with each PR.

Agent 1: Guide Content Accuracy

Focus: guide/workflow-guide.qmd

  • All numeric claims match reality (skill count, agent count, rule count, hook count)
  • All file paths mentioned actually exist on disk
  • All skill/agent/rule names match actual directory names
  • Code examples are syntactically correct
  • Cross-references and anchors resolve
  • No stale counts from previous versions
Agent 2: Executable Code Quality

Focus: all executable code in the repo — .claude/hooks/*.py, .claude/hooks/*.sh, scripts/*.py, scripts/*.sh, .claude/scripts/*.sh. Not just .claude/hooks/ — when PR #93 added new code under scripts/, the original narrow scope meant Copilot + Codex caught 5 bugs the audit missed.

Hook-specific checks (Stop/PreToolUse/SessionStart protocols, CLAUDE_PROJECT_DIR usage, hash-length consistency) apply only to .claude/hooks/. Everything below applies to ALL executable code:

  • No remaining /tmp/ usage in anything that manages state (should use ~/.claude/sessions/)
  • Hash length consistency ([:8] across all hooks) [hooks only]
  • Proper error handling — fail-open pattern where the docstring promises it (top-level try/except with sys.exit(0)). Python read_text() must catch UnicodeError (not just OSError) if the script is promised fail-open for corrupt files. Bash set -u without set -e or explicit post-command checks does NOT catch command failures — verify.
  • Docstring-claim ↔ implementation parity. If a function's docstring describes "bidirectional parity" / "fail-open" / "exits 1 on X", the implementation must match. Common drift: one-directional implementation of a claimed-bidirectional contract; exit codes documented as one thing but returning another.
  • Config-map entries point at live targets. Keyword dicts, path maps, and rule registries should not contain dead entries (e.g. rule files that don't exist, fields the script doesn't actually read). Dead entries mislead maintainers.
  • JSON input/output correctness (stdin for input, stdout/stderr for output) [hooks only]
  • Exit code correctness. Two valid blocking protocols for Stop/PreToolUse hooks: (a) exit 2 + reason on stderr — legacy, still supported (b) exit 0 + JSON {"decision": "block", "reason": "..."} on stdout — modern; this is what log-reminder.py uses and it works correctly Non-blocking hooks always exit 0. PreCompact hooks MUST exit 0 (stdout is discarded by the harness — use stderr for diagnostics)
  • from __future__ import annotations for Python 3.8+ compatibility
  • Correct field names from hook input schema (source not type for SessionStart)
  • PreCompact hooks print to stderr (stdout is ignored)
Agent 3: Skills and Rules Consistency

Focus: .claude/skills/*/SKILL.md and .claude/rules/*.md

  • Valid YAML frontmatter in all files
  • No stale disable-model-invocation: true
  • allowed-tools values are sensible
  • allowed-tools actually covers every tool the skill body invokes. For every Task spawn, Bash command, Write/Edit call mentioned in the skill's Steps / Phases / Workflow body, verify the tool appears in the allowed-tools array. Common miss: skill body says "spawn agent-X via Task with context=fork" but Task is absent from allowed-tools — runtime permission error or silent bypass. Caught this class of bug after Codex/Copilot flagged it on PR #92 (4 skills promised Task in their Post-Flight sections but 3 of 4 had no Task permission).
  • Rule paths: scope matches skill implementation. If rule X lists skill Y in paths:, verify skill Y actually implements the protocol rule X mandates. A rule claiming a skill follows a protocol is meaningless if the skill doesn't.
  • Rule paths: reference existing directories
  • No contradictions between rules
  • CLAUDE.md skills table matches actual skill directories 1:1
  • All templates referenced in rules/guide exist in templates/
Agent 4: Cross-Document Consistency

Focus: README.md, docs/index.html, docs/workflow-guide.html

  • All feature counts agree across all 3 documents
  • All links point to valid targets
  • License section matches LICENSE file
  • Directory tree matches actual structure
  • No stale counts from previous versions

PHASE 2: Triage Findings

Categorize each finding:

  • Genuine bug: Fix immediately
  • False alarm: Discard (document WHY it's false for future rounds)

Common false alarms to watch for:

  • Quarto callout ## Title inside ::: divs — this is standard syntax, NOT a heading bug
  • allowed-tools linter warning — known linter bug (Claude Code issue #25380), field IS valid
  • Counts in old session logs — these are historical records, not user-facing docs
  • Counts in CHANGELOG.md under past version headings — those are snapshots; do NOT update
  • log-reminder.py outputting {"decision": "block"} with sys.exit(0) — this IS the modern Claude Code Stop-hook block protocol, NOT a bug

Count drift specifically: search for every phrasing variant. A common failure mode is that replace_all on one phrasing (e.g., "26 skills") misses sibling phrasings in the same repo. When checking counts, grep for ALL of:

  • "N skills", "N skill " (with space)
  • "N slash commands"
  • "N specialized" (as in "N specialized agents")
  • "template's N" (informal count in prose)
  • Commas/conjunctions: "skills," vs "skills, and" are treated as different strings by replace_all Verify zero matches for the OLD number across the whole tree before declaring clean.

PHASE 3: Fix All Issues

Apply fixes in parallel where possible. For each fix:

  1. Read the file first (required by Edit tool)
  2. Apply the fix
  3. Verify the fix (grep for stale values, check syntax)

PHASE 4: Re-render if Guide Changed

If guide/workflow-guide.qmd was modified:

quarto render guide/workflow-guide.qmd
cp guide/workflow-guide.html docs/workflow-guide.html

PHASE 5: Loop or Declare Clean

After fixing, launch a fresh set of 4 agents to verify.

  • If new issues found → fix and loop again
  • If zero genuine issues → declare clean and report summary

Max loops: 5 (to prevent infinite cycling)

Key Lessons from Past Audits

These are real bugs found across 7 rounds — check for these specifically:

Bug PatternWhere to CheckWhat Went Wrong
Stale counts ("19 skills" → "21")Guide, README, landing pageAdded skills but didn't update all mentions
Hook exit codesAll Python hooksExit 2 in PreCompact silently discards stdout
Hook field namespost-compact-restore.pySessionStart uses source, not type
State in /tmp/All Python hooksShould use ~/.claude/sessions/<hash>/
Hash length mismatchAll Python hooksSome used [:12], others [:8]
Missing fail-openPython hooks __main__Unhandled exception → exit 1 → confusing behavior
Python 3.10+ syntaxType hints like `dictNone`
Missing directoriesquality_reports/specs/Referenced in rules but never created
Always-on rule listingGuide + READMEmeta-governance omitted from listings
macOS-only commandsSkills, rulesopen without xdg-open fallback
Stale hook referencesRules, guide, CHANGELOG, settings.jsonRemoved hooks still mentioned somewhere

Output Format

After each round, report:

## Round N Audit Results

### Issues Found: X genuine, Y false alarms

| # | Severity | File | Issue | Status |
|---|----------|------|-------|--------|
| 1 | Critical | file.py:42 | Description | Fixed |
| 2 | Medium | file.qmd:100 | Description | Fixed |

### Verification
- [ ] No stale counts (grep confirms)
- [ ] All hooks have fail-open + future annotations
- [ ] Guide renders successfully
- [ ] docs/ updated

### Result: [CLEAN | N issues remaining]

平台分布

Codex

34.01%
按下载量换算42

Claude

32.95%
按下载量换算41

Cursor

20.98%
按下载量换算26

Gemini CLI

9.35%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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/pedrohcgs/claude-code-my-workflow --skill deep-audit 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。目前只有一个公开来源,建议同时核对源仓库。

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