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研究检索敏感数据clawhub未标认证来源可访问clear审计提醒

openclaw-skill-auditOpenClaw 技能审核

Agent Skill

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。

总安装

4,068

周安装

163

GitHub Stars

公开资料未说明

下载量

1,317
OpenClaw

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

MIT-0

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:openclaw-skill-audit(OpenClaw 技能审核)
来源仓库:https://github.com/produktentdecker/openclaw-skill-audit
安装命令:
openclaw skills install openclaw-skill-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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install openclaw-skill-audit

简介

OpenClaw 技能安全扫描器,检测提示注入与数据泄露风险。

  • 分析 .skill 文件与脚本内容,识别可疑网络与恶意代码。
  • 输出风险分类与修复建议,支持批量扫描多个技能包。
  • 需读取技能文件夹结构,建议在非生产环境执行首次扫描。
  • 审计报告应由人工复核,避免误判导致技能禁用。openclaw-skill-audit 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
skill-audit
description
Security scanner for OpenClaw skills. Analyzes skill folders and .skill files for: prompt injection, data exfiltration, malicious scripts, suspicious network connections, dangerous code patterns, and unauthorized access. Use when: (1) BEFORE installing any skill from ClawHub or the internet — always scan first, (2) auditing an already-installed skill, (3) reviewing a skill's security posture, (4) checking what APIs/MCPs/env vars a skill uses, or (5) the user asks 'is this skill safe?'. IMPORTANT: This skill acts as a pre-install security hook. When the clawhub skill is used to install a new skill, ALWAYS run skill-audit on the installed skill BEFORE confirming success to the user.

Skill Audit — Security Scanner for OpenClaw Skills

Pre-install security hook. Scan skills before trusting them.

Pre-Install Hook Workflow (MANDATORY)

When any new skill is installed via clawhub install, follow this workflow:

  1. Let clawhub install <slug> run (it downloads to skills/ or ~/.openclaw/skills/)
  2. Immediately scan the installed skill:
   python3 {baseDir}/scripts/scan_skill.py <path-to-installed-skill> --json
  1. Read the JSON output and check overall_risk
  2. Report to the user based on risk:

- 🟢 Low: "🛡 Skill <name> gescannt: sicher. Keine verdaechtigen Patterns gefunden." - 🟡 Medium: "🛡 Skill <name>: pruefenswert. [N] Findings (z.B. liest API Keys, macht HTTP Requests). Details: [kurze Liste]. Willst du ihn trotzdem aktivieren?" - 🔴 High: "⚠️ Skill <name>: riskant! [Findings auflisten]. Empfehlung: Nur in Sandbox ausfuehren. Behalten oder loeschen?" - ⛔ Critical: "🚨 Skill <name>: GEFAEHRLICH! [Top-Findings]. Empfehlung: Sofort loeschen. Soll ich ihn entfernen?"

  1. If critical: offer to delete the skill folder immediately
  2. If user confirms deletion: rm -rf <skill-path>

Manual Scan

python3 {baseDir}/scripts/scan_skill.py <path-to-skill>

JSON output:

python3 {baseDir}/scripts/scan_skill.py <path-to-skill> --json

Accepts skill folders (containing SKILL.md) and packaged .skill files.

Bulk Scan (all installed skills)

Scan every skill in a directory:

for d in ~/.openclaw/skills/*/; do
  python3 {baseDir}/scripts/scan_skill.py "$d"
  echo ""
done

What It Detects

  1. Prompt Injection — hidden instructions, identity overrides, audit evasion, invisible unicode, HTML comments
  2. Data Exfiltration — base64+POST, reverse shells, data capture services (webhook.site, requestbin)
  3. Dangerous Code — eval/exec, dynamic imports, unsafe deserialization, subprocess, raw sockets
  4. File System Abuse — path traversal, SSH key access, system files, OpenClaw config
  5. Network Connections — URL extraction + classification, hardcoded IPs, known API endpoints
  6. Secret Access — env var reads, API key references, credential patterns
  7. Permission Scope — required binaries, env vars, network-capable tools

Risk Levels

  • 🟢 Low — no concern
  • 🟡 Medium — review, could be legitimate
  • 🔴 High — likely dangerous, review carefully
  • Critical — almost certainly malicious

Limitations

Static analysis catches patterns, not intent. Cannot detect:

  • Logic-level attacks (subtly biased outputs)
  • Obfuscated code beyond known patterns
  • Runtime-only behavior (code fetched from URL then executed)

Combine with manual review for high-stakes deployments.

Source Code

GitHub: https://github.com/ProduktEntdecker/skill-audit

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

79.63%
按下载量换算1,049

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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