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fleet-auditor车队审计员

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

1,063

周安装

43

GitHub Stars

721

下载量

334
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:fleet-auditor(车队审计员)
来源仓库:https://github.com/alexgreensh/token-optimizer
仓库路径:skills/fleet-auditor
安装命令:
npx skills add https://github.com/alexgreensh/token-optimizer --skill fleet-auditor
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/alexgreensh/token-optimizer --skill fleet-auditor

简介

fleet-auditor 用于辅助安全审计、权限检查和凭据风险排查。

  • 适合梳理敏感配置、分析鉴权逻辑或生成安全复核清单。
  • 使用时不能将工具输出直接当作最终结论,需确认最小权限和操作边界。
  • 通过 npx skills add 命令从指定仓库安装,需结合原始 README 核验具体用法。
  • 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。

SKILL.md

Fleet Auditor: Cross-Platform Agent Token Waste Auditor

Detects installed agent systems, collects token usage data, identifies waste patterns, and recommends fixes with dollar savings estimates. Everyone tracks. Nobody coaches. Until now.

Use when: Running multiple agent systems, spending $2-5/day on agents, suspecting idle heartbeats are burning tokens, or want a cross-system cost audit.


Phase 0: Initialize

  1. Resolve fleet.py path (works for both skill and plugin installs):
FLEET_PY=""
for f in "$HOME/.claude/skills/fleet-auditor/scripts/fleet.py" \
         "$HOME/.claude/plugins/cache"/*/token-optimizer/*/skills/fleet-auditor/scripts/fleet.py; do
  [ -f "$f" ] && FLEET_PY="$f" && break
done
[ -z "$FLEET_PY" ] && { echo "[Error] fleet.py not found. Is Fleet Auditor installed?"; exit 1; }
echo "Using: $FLEET_PY"

Use $FLEET_PY for all subsequent fleet.py calls.

  1. Detect systems:
python3 $FLEET_PY detect --json

Parse the JSON output. Report what was found.

If nothing detected, explain: "No agent systems found. Fleet Auditor supports: Claude Code, OpenClaw, NanoClaw, Hermes, OpenCode, IronClaw."


Phase 1: Scan

Collect token usage data from detected systems:

python3 $FLEET_PY scan --days 30

Report how many runs were collected per system. If this is the first scan, it may take a moment to parse all session files.


Phase 2: Audit

Run waste pattern detection:

python3 $FLEET_PY audit --json

Parse the JSON output. Present findings ordered by severity and monthly savings.

If no waste found: "Your fleet looks clean. No significant waste patterns detected."

OpenClaw Security + Cost Check (March 2026):

  • If OpenClaw is detected and version < 2026.3.12: flag as HIGH severity. 31 CVEs including ClawJacked (full agent takeover). Compromised instances run rogue agents that burn tokens undetected. Recommend immediate upgrade.
  • Check for ClawHub skills matching ClawHavoc campaign patterns (1,184+ malicious skills). These skills exfiltrate data via extra API calls, inflating token spend.
  • Flag if gateway rate limiting is disabled (CVE-2026-25253). Without it, brute-force attacks can spawn unlimited agent sessions. Recommend: openclaw config set security.rateLimit.enabled true

Phase 3: Present Findings

[Fleet Auditor Results]

SYSTEMS DETECTED
- Claude Code: X runs ($Y.YY)
- OpenClaw: X runs ($Y.YY)

WASTE PATTERNS FOUND
1. [SEVERITY] Description
   Est. savings: $X.XX/month
   Fix: recommendation

2. [SEVERITY] Description
   ...

TOTAL POTENTIAL SAVINGS: $X.XX/month

Ready to act? I can:
1. Show detailed fix snippets for each finding
2. Generate the fleet dashboard for visual analysis
3. Run /token-optimizer for deeper Claude Code optimization

Phase 4: Dashboard (optional)

If user wants visual analysis:

python3 $FLEET_PY dashboard

This generates ~/.claude/_backups/token-optimizer/fleet-dashboard.html and opens it in the browser.


Phase 5: Deep Dive (optional)

For Claude Code specifically, offer /token-optimizer for full audit (CLAUDE.md, skills, MCP, hooks, etc.).

For other systems, show the fix snippets from the audit and guide the user through implementing them.


Reference Files

PhaseRead
Adapter developmentreferences/fleet-systems.md
Detector developmentreferences/waste-patterns.md

Error Handling

  • No systems detected: Report cleanly, list supported systems
  • Empty scan results: System detected but no session data in window. Suggest increasing --days
  • Permission errors: Report which files couldn't be read, continue with available data
  • Corrupted data: Skip bad files, report count of skipped files
  • fleet.py not found: Check both skill and plugin install paths

Core Rules

  • Quantify everything in dollars AND tokens
  • Never read or expose message content (privacy-first)
  • Report confidence levels alongside findings
  • Suppress findings below 0.4 confidence threshold
  • Always show fix snippets with recommendations
  • Frame savings as monthly recurring, not one-time

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

36.43%
按下载量换算122

Claude

32.17%
按下载量换算107

Cursor

17.42%
按下载量换算58

Gemini CLI

8.54%
按下载量换算29

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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