skill-security

name	skill-security
description	Security scan for Claude Code skills using SkillSpector. Detects 64 vulnerability patterns — prompt injection, data exfiltration, MCP tool poisoning, supply chain, taint tracking, and more — before a skill is installed. Requires Python 3.12+ with `pip install skillspector`, or Docker as a fallback.
user-invocable	true
model	sonnet
context	fork
allowed-tools	Read Glob Grep Bash
effort	medium
argument-hint	["<path>|--llm"]

Scope: scan a skill package (SKILL.md + any associated scripts) for security vulnerabilities before installation. Covers 64 patterns across 16 categories using SkillSpector (NVIDIA, Apache 2.0).

Out of scope: application security audits (use /security-audit instead), scanning the project's own source code, fixing identified vulnerabilities.

Do NOT modify any file during the scan.

---

Step 0 — Preflight: verify SkillSpector availability

Check availability in this order and announce which execution path will be used:

Path A — SkillSpector (pip)

python3 --version 2>&1 | head -1
pip show skillspector 2>&1 | grep -E "^(Name|Version):"

If both return successfully (Python 3.12+ AND skillspector installed): proceed with Path A. Announce: Using SkillSpector [version] via pip.

Path B — Docker fallback

If Path A is not available:

docker --version 2>&1 | head -1
docker image ls skillspector 2>&1 | grep -c skillspector

If Docker is available: instruct the user to build the image if not already built:

Docker image not found. Build it with:
  git clone https://github.com/NVIDIA/skillspector.git /tmp/skillspector
  docker build -t skillspector /tmp/skillspector
Then re-run this skill.

If the image exists: proceed with Path B. Announce: Using SkillSpector via Docker.

Neither available

If neither Path A nor Path B is usable, stop and output:

SkillSpector is not available. Install it with one of:

  pip install skillspector          # requires Python 3.12+
  pipx install skillspector         # isolated install

Or build the Docker image:
  git clone https://github.com/NVIDIA/skillspector.git /tmp/skillspector
  docker build -t skillspector /tmp/skillspector

Then re-run: /skill-security [path]

Do not proceed further.

---

Step 1 — Resolve scan target

Parse $ARGUMENTS for a path and the --llm flag.

Argument pattern	Scan target	LLM mode
(none)	Current directory (.)	disabled
./path/to/skill/	Specified directory	disabled
./SKILL.md	Specified file	disabled
--llm	Current directory	enabled
./path/to/skill/ --llm	Specified directory	enabled

After resolving the path, verify it exists:

ls -la <resolved_path> 2>&1 | head -5

If the path does not exist: stop with Target not found: <path>. Pass a valid skill directory or SKILL.md file.

Announce: Scanning: <resolved_path> | LLM analysis: [enabled|disabled]

---

Step 2 — Execute static scan

Run the static scan (no LLM) using the available execution path. Capture JSON output for structured parsing.

Path A (pip)

skillspector scan <resolved_path> --no-llm --format json 2>&1

Path B (Docker)

docker run --rm -v "$PWD:/scan" skillspector scan <resolved_path> --no-llm --format json 2>&1

If the command exits with code 2 (error): report the stderr output and stop. A code 1 means risk_score > 50 and is expected — do not treat it as an error.

Store the JSON output for Step 4 parsing.

---

Step 3 — LLM analysis (opt-in)

Only run this step if --llm was passed in $ARGUMENTS.

Check if an LLM provider credential is available:

echo ${ANTHROPIC_API_KEY:+set} ${OPENAI_API_KEY:+set} 2>/dev/null

If ANTHROPIC_API_KEY is set:

SKILLSPECTOR_PROVIDER=anthropic skillspector scan <resolved_path> --format json 2>&1

If OPENAI_API_KEY is set (and no Anthropic key):

SKILLSPECTOR_PROVIDER=openai skillspector scan <resolved_path> --format json 2>&1

If no key is found: output a warning and proceed with the static-only results from Step 2:

⚠  --llm requested but no API key found.
   Set ANTHROPIC_API_KEY or OPENAI_API_KEY and re-run to enable semantic analysis.
   Showing static-only results (precision may be lower).

Replace the Step 2 JSON output with the LLM-enhanced JSON if the LLM scan succeeded.

---

Step 4 — Present report

Parse the JSON output and present the report in this format. Use the actual values from the JSON fields risk_score, risk_severity, risk_recommendation, and filtered_findings (or findings if filtered_findings is absent).

---

## Skill Security Report

Skill:       <basename of resolved_path>
Source:      <resolved_path>
Analysis:    [Static only | Static + LLM (claude-opus-4-6)]

Risk Score:  <risk_score>/100
Severity:    <risk_severity>
Verdict:     <risk_recommendation>

Severity badge

risk_severity	Display
LOW	✅ LOW — SAFE TO INSTALL
MEDIUM	⚠️ MEDIUM — REVIEW BEFORE INSTALLING
HIGH	🔴 HIGH — DO NOT INSTALL
CRITICAL	🚨 CRITICAL — DO NOT INSTALL

Findings (group by severity: CRITICAL → HIGH → MEDIUM → LOW)

For each finding in the JSON array, output:

[SEVERITY] <rule_id> — <message>
  Location:    <file>:<start_line>
  Confidence:  <confidence as percentage>%
  Finding:     <finding (matched snippet, if present)>
  Explanation: <explanation>
  Remediation: <remediation>

If there are zero findings: output No security issues detected.

Score breakdown

Explain the score only when risk_score > 0:

Score breakdown:
  CRITICAL issues × 50pts: <count>
  HIGH issues × 25pts:     <count>
  MEDIUM issues × 10pts:   <count>
  LOW issues × 5pts:       <count>
  Script multiplier (1.3×): [applied|not applied]

---

Closing

If risk_recommendation is DO NOT INSTALL:

⛔ This skill has a risk score of <risk_score>/100 and should NOT be installed.
   Address all CRITICAL and HIGH findings before using it.

If risk_recommendation is CAUTION:

⚠️  Review the findings above before installing this skill.
   Run with --llm for deeper semantic analysis (reduces false positives to ~13%).

If risk_recommendation is SAFE:

✅ This skill passed the security scan. Safe to install.
   Tip: run with --llm for semantic analysis if the skill includes executable code.