Capability Graph Mapper

v1.0.0

Helps map the composite permission surface across AI agent skill dependency chains. Traces what each skill can do individually, then computes what they can d...

1· 641·1 current·1 all-time

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for andyxinweiminicloud/capability-graph-mapper.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Capability Graph Mapper" (andyxinweiminicloud/capability-graph-mapper) from ClawHub.
Skill page: https://clawhub.ai/andyxinweiminicloud/capability-graph-mapper
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required binaries: curl, python3
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Canonical install target

openclaw skills install andyxinweiminicloud/capability-graph-mapper

ClawHub CLI

Package manager switcher

npx clawhub@latest install capability-graph-mapper
Security Scan
VirusTotalVirusTotal
Benign
View report →
OpenClawOpenClaw
Benign
medium confidence
Purpose & Capability
Name, description, and SKILL.md all describe analyzing installed skills and computing composition paths. The declared requirements (curl, python3) are plausible for fetching manifests and doing analysis, though no code is provided — this is explainable for an instruction-only skill that invokes system tools.
Instruction Scope
SKILL.md focuses on enumerating declared permissions, pairwise/transitive composition, scoring, and delta analysis. It does not instruct reading arbitrary unrelated files, accessing environment variables, or transmitting results to unexpected remote endpoints. The instructions imply the agent will need access to skill manifests (local or remote), so runtime access to those manifests is expected.
Install Mechanism
No install spec and no code files — lowest-risk form. The skill is instruction-only, so nothing will be written to disk by an installer. The only install-related artifact is the requirement that curl and python3 be present on PATH.
Credentials
The skill requests no environment variables, no credential fields, and no config paths. That is proportionate to its stated purpose. Note: to perform analysis it may need read access to skill manifests or the ability to call network endpoints (curl) — these are not declared as environment/credential requirements but are reasonable runtime needs.
Persistence & Privilege
always is false and the skill does not request persistent system modifications. It is user-invocable and allows autonomous invocation by default (platform normal). There is no indication it modifies other skills' configurations or requires elevated, persistent privileges.
Assessment
This skill is coherent: it declares and documents its purpose and needs few resources. Before installing, confirm where the agent will fetch skill manifests from and whether the analysis results are ever transmitted externally. Specifically: 1) Ask the publisher (or your admin) whether the tool will fetch manifests from the network and, if so, what endpoints it calls. 2) If you plan to run it against a live agent, ensure the agent only grants it read access to skill manifests (not to secrets, .env files, or arbitrary filesystem paths). 3) Because it uses curl/python at runtime (per metadata), run it in a sandbox or with least privilege if possible. 4) If you need higher assurance, request a source repo or a reproducible implementation so you can audit exactly what commands it runs.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🕸️ Clawdis
Binscurl, python3
latestvk970x226b6p4f1sp5pq79a53t581n8jz
641downloads
1stars
1versions
Updated 1mo ago
v1.0.0
MIT-0

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.

Comments

Loading comments...