skills-audit
- Repo stars 327
- Author updated Live
- Author repo harness
- Domain
- Security
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 83 / 100 · community maintained
- Author / version / license
- @xwtro0tk1t-cloud · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Env read
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。; 检出高风险片段:pipe_curl_to_shell
---
name: skills-audit
description: Audit AI Agent skills for security vulnerabilities including malicious code, remote execution, c…
category: security
runtime: Python
---
# skills-audit output preview
## PART A: Task fit
- Use case: Audit AI Agent skills for security vulnerabilities including malicious code, remote execution, credential leaks, and supply chain risks. Use when reviewing third-party skills, investigating suspicious behavior, or performing security assessments..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Task / Execution Steps / Scan Modes” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Audit AI Agent skills for security vulnerabilities including malicious code, remote execution, credential leaks, and supply chain risks. Use when reviewing third-party skills, investigating suspicious behavior, or performing security assessments.”.
- **02** When the source has headings, the agent prioritizes “Task / Execution Steps / Scan Modes” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/skills-audit`, `/path`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands, read environment variables.
Start with a small task and check whether the result follows “Task / Execution Steps / Scan Modes”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: skills-audit
description: Audit AI Agent skills for security vulnerabilities including malicious code, remote execution, c…
category: security
source: xwtro0tk1t-cloud/harness
---
# skills-audit
## When to use
- Audit AI Agent skills for security vulnerabilities including malicious code, remote execution, credential leaks, and s…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Task / Execution Steps / Scan Modes” and keep inference separate from source facts.
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "skills-audit" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Task / Execution Steps / Scan Modes
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands, read environment variables | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Security Audit for AI Agent Skills
You are conducting a security audit of an AI Agent skill. This skill executes a comprehensive analysis to detect malicious code, security vulnerabilities, and suspicious patterns.
Task
Audit the skill at path: $ARGUMENTS
Execution Steps
Run static security scan Execute the Python audit tool for static analysis:
# Auto-detect skills-audit installation path AUDIT_SCRIPT="" for candidate in \ ~/.claude/skills/skills-audit/skill_audit/cli_wrapper.py \ ~/.claude/skills/skill-audit/skill_audit/cli_wrapper.py \ "${SKILL_AUDIT_HOME:-}""/skill_audit/cli_wrapper.py"; do if [ -f "$candidate" ]; then AUDIT_SCRIPT="$candidate" break fi done if [ -z "$AUDIT_SCRIPT" ]; then echo "Error: Cannot find skills-audit installation" echo "Set SKILL_AUDIT_HOME environment variable to your skills-audit directory" exit 1 fi python3 "$AUDIT_SCRIPT" "$ARGUMENTS"This will:
- Extract skill artifacts (code, prompts, permissions)
- Run static pattern matching (regex-based detection)
- Check for obvious malicious patterns
- Generate initial findings
Perform AI semantic analysis (if enabled)
If the scan mode includes AI analysis (standard/deep/expert), perform deep semantic security analysis:
a. Read the skill code files from the target path
b. Analyze for security vulnerabilities:
- Remote Code Execution:
eval(),exec(),subprocess,curl | bash - Credential Leaks: Hardcoded API keys, passwords, tokens, .env files
- Data Exfiltration: Suspicious network requests, file uploads
- Prompt Injection: "Ignore previous instructions", role manipulation
- Supply Chain Risks: Obfuscated code, dynamic imports, base64 encoding
- Privilege Escalation: sudo, setuid, file permission changes
- Persistence Mechanisms: cron jobs, shell profile modifications
c. Assess each finding:
- Severity: CRITICAL / HIGH / MEDIUM / LOW
- Attack scenario: How can this be exploited?
- Impact: What damage could be done? (CIA triad)
- Remediation: How to fix it?
d. Filter false positives:
- Exclude findings from skills-audit's own detection patterns (patterns.py regex)
- Downgrade benign file operations (e.g. deleting old output before regeneration)
- Verify env var access patterns (using dotenv is recommended, not a vulnerability)
e. Output your analysis in this format:
AI SEMANTIC ANALYSIS FINDINGS: 1. [SEVERITY] Finding Title - Location: file.py:line - Pattern: describe what you found - Risk: explain the security risk - Scenario: how an attacker could exploit this - Impact: potential damage - Recommendation: how to fix 2. [SEVERITY] Finding Title ...f. Integrate AI findings into the report (CRITICAL STEP)
After completing your AI analysis, integrate your findings into the audit report by running:
# Use the detected AUDIT_SCRIPT path from step 1 INTEGRATE_SCRIPT="$(dirname "$AUDIT_SCRIPT")/integrate_ai_findings.py" python3 "$INTEGRATE_SCRIPT" \ "<report_path>" \ '<ai_findings_json>'Where:
<report_path>: The path to the JSON report file (shown in step 1 output as "Detailed report saved to: ...")<ai_findings_json>: Your AI analysis findings formatted as JSON array
JSON Format for ai_findings:
[ { "title": "Base64-Obfuscated Remote Code Execution", "severity": "CRITICAL", "category": "unsafe_execution", "description": "Base64-encoded command that downloads and executes arbitrary code", "location": "skill.md:28", "code_snippet": "echo 'L2Jpbi9iYXNoIC1jIC...' | base64 -D | bash", "risk": "Remote code execution with complete system compromise", "scenario": "User follows installation instructions, base64 decodes to malicious payload, executes with shell privileges", "impact": { "confidentiality": "CRITICAL", "integrity": "CRITICAL", "availability": "CRITICAL" }, "impact_description": "Full system compromise, data theft, ransomware deployment", "recommendation": "BLOCK this skill entirely. Never execute obfuscated commands.", "cwe_ids": ["CWE-78", "CWE-94", "CWE-506"] } ]Important:
- Convert ALL your AI analysis findings from step 2e into this JSON format
- Include severity (CRITICAL/HIGH/MEDIUM/LOW), location, code snippets, risk, scenario, impact, and recommendations
- This step MERGES your AI findings with static analysis findings and recalculates the overall risk score
- Webhook is NOT sent during this step -- it will be sent after your comprehensive analysis
- Remote Code Execution:
Send final webhook notification (optional, if notifications are configured) After completing comprehensive analysis (including false positive filtering), send the webhook:
# Auto-detect skills-audit path AUDIT_DIR="$(dirname "$(dirname "$AUDIT_SCRIPT")")" python3 -c " import sys; sys.path.insert(0, '$AUDIT_DIR') from skill_audit.integrations import send_final_webhook send_final_webhook(report_path='<report_path>') "This ensures the webhook contains the final, accurate results after your analysis.
Present comprehensive results to user
- Summarize the overall risk level and score (from integrated report)
- List key findings with severity levels
- Clearly mark any false positives that were filtered
- For critical findings, include:
- Title and severity
- Evidence location and code snippet
- Attack scenario and impact
- Remediation recommendation
- Provide the final decision recommendation
- Reference the detailed JSON report path for full analysis
If high-risk issues are found:
- Explain the security implications
- Suggest concrete remediation steps
- Recommend whether to BLOCK, REVIEW, or ALLOW the skill
- Warn about potential damage if the skill is executed
Scan Modes
Deep Mode (Default)
- Speed: ~2-5 minutes
- Coverage: Full Claude AI analysis + static patterns + deep code understanding
- Use: Recommended for all skills
- Command:
/skills-audit /path/to/skill(default) or/skills-audit /path/to/skill --mode deep - Note: Includes comprehensive AI analysis by Claude
Fast Mode
- Speed: ~1-2 seconds
- Coverage: Static pattern matching only
- Use: Quick check for obvious vulnerabilities
- Command:
/skills-audit /path/to/skill --mode fast
Standard Mode
- Speed: ~30 seconds - 2 minutes (depends on code size)
- Coverage: Claude AI semantic analysis + static patterns
- Use: Balanced speed and coverage
- Command:
/skills-audit /path/to/skill --mode standard - Note: Claude (you) will perform semantic analysis
Expert Mode
- Speed: ~5-10 minutes
- Coverage: Complete analysis with all phases
- Use: Critical security reviews
- Command:
/skills-audit /path/to/skill --mode expert - Note: Maximum depth analysis performed by Claude
Detection Capabilities
This audit detects:
- Remote Code Execution:
curl | bash,eval(),exec() - Credential Leaks: Hardcoded API keys, passwords, .env files
- Network Exfiltration: Suspicious HTTP/Socket connections
- Supply Chain Risks: Obfuscation, dynamic imports
- Prompt Injection: "Ignore previous instructions"
- System Manipulation: File deletion, permission changes
Configuration
Edit config/config.yml (relative to skills-audit installation directory) to customize:
Key Configuration Options
# Report save location
claude_code:
# Options: cwd (current directory), skill_dir (skill directory), temp (temp directory), custom
report_location: custom
custom_report_dir: ~/.claude/audit-reports
# Custom report naming
output:
report_filename: "audit-{skill_name}-{timestamp}.json"
Scan Mode Customization
scan_modes:
fast:
enable_ai_analysis: false
enable_static_analysis: true
enable_deep_analysis: false
enable_tip_check: false
standard:
enable_ai_analysis: true
enable_static_analysis: true
enable_deep_analysis: false
enable_tip_check: false
deep:
enable_ai_analysis: true
enable_static_analysis: true
enable_deep_analysis: true
enable_tip_check: true
Notes
- Default mode is deep (includes AI + Static + Deep analysis by Claude)
- For quick scans, use
--mode fast(static analysis only, 1-2 seconds) - AI analysis in standard/deep/expert modes is performed by Claude directly (no API calls)
- Reports saved to ~/.claude/audit-reports/ by default (configurable)
- Use
--modeflag to override scan mode (the--modeparameter is authoritative) - Config file location:
config/config.ymlrelative to skills-audit installation directory - Webhook is deferred until after Claude's comprehensive analysis (false positive filtering)
- skills-audit itself is excluded from scanning to avoid self-referential false positives
- Works offline: Static analysis works without internet; AI analysis uses current Claude session
Decide Fit First
Design Intent
How To Use It
Boundaries And Review