mcpField
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Agent Skill Scanner

Agent Skill Scanner

Local
github.com/rexcolemanAI & Agents0Updated 30w ago

About Agent Skill Scanner

Agent Skill Scanner is a local MCP server for checking OpenClaw SKILL.md files and MCP tool definitions for known security patterns. It is aimed at people reviewing agent skills, with 22 pattern-based rules and findings that include severity, rule ID, description, and evidence.

Key features

  1. 01Scan one skill file or a directory
  2. 02Detect prompt injection and escalation patterns
  3. 03Find data exfiltration and encoded payloads
  4. 04Check composition risks
  5. 05Return findings with severity and evidence

Use cases

  1. 01Review an OpenClaw SKILL.md before use
  2. 02Audit MCP tool definitions for known risks
  3. 03Scan a directory of agent skill files

MCP support

Exposes tools to MCP clients for scanning individual skill files and directories. It runs locally over stdio and checks OpenClaw SKILL.md and MCP tool definition formats.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

agent-skill-scanner MCP Server

Last updated: 2026-03-31

Scan OpenClaw SKILL.md and Model Context Protocol (MCP) tool definition files for security vulnerabilities — directly from Claude Code.

22 detection rules across prompt injection, capability escalation, data exfiltration, encoded payloads, and composition risks. This is the only scanner targeting agent skill file formats specifically. Generic Static Application Security Testing (SAST) tools produce zero detections on these formats.

Install

Requires Python 3.10+ and the scanner engine:

pip install agent-skill-scanner

Configure in Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "agent-skill-scanner": {
      "command": "python3",
      "args": ["/path/to/agent-skill-scan-mcp/server.py"]
    }
  }
}

Replace /path/to/ with the actual path where you cloned this repo.

Tools

scan_skill_file

Scan a single skill file for security vulnerabilities.

scan_skill_file(file_path="/path/to/SKILL.md")

Returns findings with severity, rule ID, description, and evidence.

scan_directory

Recursively find and scan all agent skill files in a directory.

scan_directory(directory_path="/path/to/skills/")

Returns aggregated findings across all discovered skill files.

What it detects

22 rules across 5 categories:

CategoryExamples
Prompt injectionSystem prompt override, role hijacking, instruction injection
Capability escalationPrivilege escalation, shell spawning, persistence mechanisms
Data exfiltrationCredential access, environment variable reads, outbound transfer
Encoded payloadsBase64 commands, hex payloads, obfuscated strings
Composition risksUnrestricted tool chaining, cross-skill data flow, trust violations

Differentiator

This scanner targets OpenClaw SKILL.md and MCP tool definition formats — markdown-embedded code and YAML skill configurations that generic SAST tools (semgrep, CodeQL) miss entirely. If you're scanning general Python/JavaScript code, use Snyk or semgrep. If you're scanning agent skill files, this is the only tool that covers the format.

Trust & Security

This server runs locally via stdio. No network calls beyond the initial pip install. No data collection. No telemetry.

Source is fully auditable in this repo. The scanner engine source is at github.com/rexcoleman/agent-skill-scanner.

Limitations

  • Pattern-based detection only — no semantic analysis
  • Designed for OpenClaw SKILL.md and MCP tool definitions
  • Rules cover known attack patterns from published research, not zero-days

Links

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.