Token导航 LogoToken导航

Agent Skill · 效率

capability-graph-mapper(能力图映射器)

capability-graph-mapper 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

capability-graph-mapper 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 3,011。

总安装 20,227周安装 860GitHub Stars 1下载 7,086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-0
版本
1.0.0

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:capability-graph-mapper(能力图映射器)
来源仓库:https://github.com/andyxinweiminicloud/capability-graph-mapper
安装命令:
openclaw skills install capability-graph-mapper
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 capability-graph-mapper

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

跨技能依赖链映射复合权限边界和能力组合效果。帮助理解多个技能协同工作时产生的整体行为。自动追踪每个技能的独立能力并计算联合影响。需读取技能配置文件和相关元数据。capability-graph-mapper 属于效率类 Skill,可作为该场景下的辅助能力补充。建议在部署复杂技能集前使用此工具评估风险。

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 OpenClaw;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 openclaw skills install capability-graph-mapper;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

    它需要接触敏感数据,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

capability-graph-mapper 怎么安装?

在终端执行 openclaw skills install capability-graph-mapper。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

capability-graph-mapper 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

capability-graph-mapper 需要什么权限?

权限级别是接触敏感数据。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。

capability-graph-mapper 是免费的吗?

来源标注的许可证是 MIT-0。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。

capability-graph-mapper 用的人多吗?

公开数据是累计安装 20,227 次,近一周安装 860 次,下载 7,086 次,GitHub 1 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。

capability-graph-mapper 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
capability-graph-mapper

能力图映射器 · 本页

20,2271接触敏感数据MIT-0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

name
capability-graph-mapper
description
>
version
1.0.0
metadata
openclaw
requires
bins
[curl, python3]
env
[]
emoji
🕸️

Your Agent Has 12 Skills — Do You Know What They Can Do Together?

Helps map composite permission surfaces across skill dependency chains, revealing emergent capabilities that no single skill declares.

Problem

Individual skill permissions look reasonable in isolation. A file-reader skill reads files. An HTTP client skill sends requests. A JSON parser skill transforms data. Each one passes a security review on its own.

But install all three in the same agent, and you've built a data exfiltration pipeline — read sensitive files, parse out credentials, send them to an external endpoint. Nobody approved that combination. Nobody even noticed it exists.

In traditional software, tools like npm audit map dependency trees and flag known vulnerabilities. In agent ecosystems, the risk isn't in individual dependencies — it's in the composite capability surface that emerges when skills combine. There is no npm audit for emergent agent capabilities.

What This Maps

This mapper traces the permission graph across an agent's installed skills:

  1. Permission enumeration — For each skill, extract declared capabilities: file access, network requests, shell execution, environment variable reads, credential access
  2. Pairwise composition — For every pair of skills, check if their combined capabilities create a new emergent capability (e.g., read + send = exfiltrate)
  3. Transitive chains — Trace three-hop and deeper composition paths where skill A feeds skill B feeds skill C, creating capabilities invisible at any single hop
  4. Privilege surface score — Compute a single metric: how many distinct dangerous capability combinations exist in this agent's skill set?
  5. Delta analysis — When a new skill is added, show what new composite capabilities it introduces to the existing set

How to Use

Input: Provide one of:

  • A list of skill names/slugs installed in an agent
  • A skill manifest or configuration file
  • A single skill to evaluate against a known agent profile

Output: A capability graph report containing:

  • Permission matrix (skills × capabilities)
  • Emergent capability combinations flagged as risky
  • Privilege surface score (0-100)
  • Recommendation: which skill combinations to review manually
  • Delta report if evaluating a new addition

Example

Input: Map capability surface for agent with skills: log-analyzer, http-poster, env-reader, markdown-formatter

🕸️ CAPABILITY GRAPH — 3 emergent risks detected

Permission matrix:
                    read_files  send_http  read_env  exec_shell  write_files
  log-analyzer         ✓
  http-poster                      ✓
  env-reader           ✓                     ✓
  markdown-formatter   ✓                                ✓

Emergent capability combinations:

  ⚠️ RISK 1: Data exfiltration path
     env-reader (read .env) → http-poster (send HTTP)
     Combined: Can read credentials and transmit them externally
     Severity: HIGH

  ⚠️ RISK 2: Sensitive file relay
     log-analyzer (read logs) → http-poster (send HTTP)
     Combined: Can read application logs and send contents externally
     Severity: MODERATE

  ⚠️ RISK 3: Three-hop chain
     env-reader (read secrets) → markdown-formatter (transform data)
     → http-poster (send HTTP)
     Combined: Read, obfuscate, and exfiltrate in one pipeline
     Severity: HIGH

Privilege surface score: 67/100 (elevated)

Recommendation:
  - Review whether http-poster needs to coexist with env-reader
  - Consider sandboxing env-reader's file access scope
  - The markdown-formatter → http-poster chain enables obfuscation;
    audit what markdown-formatter can output

Related Tools

  • blast-radius-estimator — estimates downstream impact when a skill turns malicious; capability-graph-mapper helps quantify *what* a compromised skill could do
  • permission-creep-scanner — checks individual skills for over-permission; this mapper checks what happens when multiple over-permissioned skills combine
  • supply-chain-poison-detector — detects poisoned individual skills; this mapper shows why a poisoned skill with network access is more dangerous in agents that also have file-read skills

Limitations

Capability graph mapping depends on accurately extracting each skill's actual permissions, which may not always match declared permissions. Skills that dynamically request capabilities at runtime may not be fully captured through static analysis. The composition risk model uses known dangerous patterns (read+send, parse+execute) but novel attack chains may not be in the pattern library. This tool helps surface emergent risks for human review — it does not guarantee detection of all possible capability combinations. Privilege surface scores are relative, not absolute measures of risk.

平台分布

OpenClaw

94.73%
按下载量换算6,713

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

接触敏感数据

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills