/skill-scanner

name: skill-scanner
description: Scan agent skills for security issues. Use when asked to "scan a skill",
  "audit a skill", "review skill security", "check skill for injection", "validate SKILL.md",
  or assess whether an agent skill is safe to install. Checks for prompt injection,
  malicious scripts, excessive permissions, secret exposure, and supply chain risks.
allowed-tools: Read, Grep, Glob, Bash

Skill Security Scanner

Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.

**Requires**: The `uv` CLI for python package management, install guide at https://docs.astral.sh/uv/getting-started/installation/

**Important**: Run all scripts from the repository root. Script paths like `scripts/scan_skill.py` are relative to this skill's root directory (the directory containing this SKILL.md), not relative to the target repository.

Bundled Script

`scripts/scan_skill.py`

Static analysis scanner that detects deterministic patterns. Outputs structured JSON.

uv run scripts/scan_skill.py <skill-directory>

Returns JSON with findings, URLs, structure info, and severity counts. The script catches patterns mechanically — your job is to evaluate intent and filter false positives.

Workflow

Phase 1: Input & Discovery

Determine the scan target:

• If the user provides a skill directory path, use it directly
• If the user names a skill, look for it under `.agents/skills/<name>/` first, then other established layouts such as `skills/<name>/` when the repo uses a canonical root skill tree, `.claude/skills/<name>/`, `plugins/*/skills/<name>/`, or another repo-managed skill root with clear prior art
• If the user says "scan all skills", discover all `*/SKILL.md` files and scan each

Validate the target contains a `SKILL.md` file. List the skill structure:

ls -la <skill-directory>/
ls <skill-directory>/references/ 2>/dev/null
ls <skill-directory>/scripts/ 2>/dev/null

Phase 2: Automated Static Scan

Run the bundled scanner:

uv run scripts/scan_skill.py <skill-directory>

Parse the JSON output. The script produces findings with severity levels, URL analysis, and structure information. Use these as leads for deeper analysis.

**Fallback**: If the script fails, proceed with manual analysis using Grep patterns from the reference files.

Phase 3: Frontmatter Validation

Read the SKILL.md and check:

• **Required fields**: `name` and `description` must be present
• **Name consistency**: `name` field should match the directory name
• **Tool assessment**: Review `allowed-tools` — is Bash justified? Are tools unrestricted (`*`)?
• **Model override**: Is a specific model forced? Why?
• **Description quality**: Does the description accurately represent what the skill does?

Phase 4: Prompt Injection Analysis

Load `references/prompt-injection-patterns.md` for context.

Review scanner findings in the "Prompt Injection" category. For each finding:

1. Read the surrounding context in the file 2. Determine if the pattern is **performing** injection (malicious) or **discussing/detecting** injection (legitimate) 3. Skills about security, testing, or education commonly reference injection patterns — this is expected

**Critical distinction**: A security review skill that lists injection patterns in its references is documenting threats, not attacking. Only flag patterns that would execute against the agent running the skill.

Phase 5: Behavioral Analysis

This phase is agent-only — no pattern matching. Read the full SKILL.md instructions and evaluate:

**Description vs. instructions alignment**:

• Does the description match what the instructions actually tell the agent to do?
• A skill described as "code formatter" that instructs the agent to read ~/.ssh is misaligned

**Config/memory poisoning**:

• Instructions to modify `CLAUDE.md`, `MEMORY.md`, `settings.json`, `.mcp.json`, or hook configurations
• Instructions to add itself to allowlists or auto-approve permissions
• Writing to `~/.claude/`, `~/.agents/`, or any agent configuration directory
• Scripts that append to global config files — the poisoned instructions persist after skill removal

**Scope creep**:

• Instructions that exceed the skill's stated purpose
• Unnecessary data gathering (reading files unrelated to the skill's function)
• Instructions to install other skills, plugins, or dependencies not mentioned in the description

**Information gathering**:

• Reading environment variables beyond what's needed
• Listing directory contents outside the skill's scope
• Accessing git history, credentials, or user data unnecessarily

**Structural attacks** (check scanner output for these):

• **Symlinks**: Files that resolve outside the skill directory — can disguise reads of `~/.ssh/id_rsa`, `~/.aws/credentials`, etc. as "example" files
• **Frontmatter hooks**: `PostToolUse`/`PreToolUse` hooks in YAML — execute shell commands automatically, the model cannot prevent it
• **`!`command`` syntax**: Runs shell commands at skill load time during template expansion, before the model sees the prompt
• **Test files**: `conftest.py`, `test_*.py`, `*.test.js` — test runners auto-discover and execute these as side effects of `pytest` or `npm test`
• **npm lifecycle hooks**: `postinstall` scripts in bundled `package.json` — run automatically on `npm install`
• **Image metadata**: PNG files with text in metadata chunks (tEXt/iTXt) — multimodal LLMs can read hidden instructions from image metadata

Phase 6: Script Analysis

If the skill has a `scripts/` directory:

1. Load `references/dangerous-code-patterns.md` for context 2. Read each script file fully (do not skip any) 3. Check scanner findings in the "Malicious Code" category 4. For each finding, evaluate:

• **Data exfiltration**: Does the script send data to external URLs? What data?
• **Reverse shells**: Socket connections with red