skill-scanner — Claude Code skill: what it does, how to install

---
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.

```bash
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:

```bash
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:

```bash
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 redirected I/O
   - **Credential theft**: Reading SSH keys, .env files, tokens from environment
   - **Dangerous execution**: eval/exec with dynamic input, shell=True with interpolation
   - **Config modification**: Writing to agent settings, shell configs, git hooks
5. Check PEP 723 `dependencies` — are they legitimate, well-known packages?
6. Verify the script's behavior matches the SKILL.md description of what it does

**Legitimate patterns**: `gh` CLI calls, `git` commands, reading project files, JSON output to stdout are normal for skill scripts.

### Phase 7: Supply Chain Assessment

Review URLs from the scanner output and any additional URLs found in scripts:

- **Trusted domains**: GitHub, PyPI, official docs — normal
- **Untrusted domains**: Unknown domains, personal sites, URL shorteners — flag for review
- **Remote instruction loading**: Any URL that fetches content to be executed or interpreted as instructions is high risk
- **Dependency downloads**: Scripts that download and execute binaries or code at runtime
- **Unverifiable sources**: References to packages or tools not on standard registries

### Phase 8: Permission Analysis

Load `references/permission-analysis.md` for the tool risk matrix.

Evaluate:

- **Least privilege**: Are all granted tools actually used in the skill instructions?
- **Tool justification**: Does the skill body reference operations that require each tool?
- **Risk level**: Rate the overall permission profile using the tier system from the reference

Example assessments:
- `Read Grep Glob` — Low risk, read-only analysis skill
- `Read Grep Glob Bash` — Medium risk, needs Bash justification (e.g., running bundled scripts)
- `Read Grep Glob Bash Write Edit WebFetch Task` — High risk, near-full access

## Confidence Levels

| Level | Criteria | Action |
|-------|----------|--------|
| **HIGH** | Pattern confirmed + malicious intent evident | Report with severity |
| **MEDIUM** | Suspicious pattern, intent unclear | Note as "Needs verification" |
| **LOW** | Theoretical, best practice only | Do not report |

**False positive awareness is critical.** The biggest risk is flagging legitimate security skills as malicious because they reference attack patterns. Always evaluate intent before reporting.

## Output Format

```markdown
## Skill Security Scan: [Skill Name]

### Summary
- **Findings**: X (Y Critical, Z High, ...)
- **Risk Level**: Critical / High / Medium / Low / Clean
- **Skill Structure**: SKILL.md only / +references / +scripts / full

### Findings

#### [SKILL-SEC-001] [Finding Type] (Severity)
- **Location**: `SKILL.md:42` or `scripts/tool.py:15`
- **Confidence**: High
- **Category**: Prompt Injection / Malicious Code / Excessive Permissions / Secret Exposure / Supply Chain / Validation
- **Issue**: [What was found]
- **Evidence**: [code snippet]
- **Risk**: [What could happen]
- **Remediation**: [How to fix]

### Needs Verification
[Medium-confidence items needing human review]

### Assessment
[Safe to install / Install with caution / Do not install]
[Brief justification for the assessment]
```

**Risk level determination**:
- **Critical**: Any high-confidence critical finding (prompt injection, credential theft, data exfiltration)
- **High**: High-confidence high-severity findings or multiple medium findings
- **Medium**: Medium-confidence findings or minor permission concerns
- **Low**: Only best-practice suggestions
- **Clean**: No findings after thorough analysis

## Reference Files

| File | Purpose |
|------|---------|
| `references/prompt-injection-patterns.md` | Injection patterns, jailbreaks, obfuscation techniques, false positive guide |
| `references/dangerous-code-patterns.md` | Script security patterns: exfiltration, shells, credential theft, eval/exec |
| `references/permission-analysis.md` | Tool risk tiers, least privilege methodology, common skill permission profiles |