# Security Audit > Implements intelligent security audit with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/security-audit` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/security-audit` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/security-audit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/security-audit --- # Security Audit Orchestrates intelligent skill selection and execution for security audit workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def build_security_audit_plan( target_url: str, scan_type: str, available_tools: List[Dict], compliance_frameworks: List[str] = ["OWASP", "NIST"] ) -> Dict: """Construct a security audit execution plan based on target and compliance requirements. Maps security requirements to specific scanning tools and configurations. Implements Law 2: Parse inputs at boundary to ensure valid audit targets. """ if not target_url or not scan_type: raise ValueError("Target URL and scan type are required for security audit") # Validate target against allowed protocols and domains parsed_target = _validate_security_target(target_url) # Map scan type to compatible tools (Law 3: Return new structures) tool_candidates = [ tool for tool in available_tools if tool["type"] == scan_type and tool["status"] == "healthy" ] if not tool_candidates: return { "plan_status": "incomplete", "reason": f"No healthy {scan_type} tools available", "fallback_tools": [t["name"] for t in available_tools if t["status"] == "maintenance"] } # Build audit configuration with compliance mappings audit_config = { "target": parsed_target, "scan_type": scan_type, "selected_tool": tool_candidates[0]["name"], "compliance_checks": compliance_frameworks, "timeout_seconds": 3600, "output_format": "json" } return audit_config ``` ### Pattern 2: Execution with Fallback ```python def execute_security_scan( audit_plan: Dict, scan_credentials: Dict, max_retries: int = 2 ) -> Dict: """Execute a security scan with domain-specific error handling and result parsing. Implements Law 4: Fail fast on credential/auth failures, don't retry blindly. Handles transient network issues common in external vulnerability scanners. """ tool_name = audit_plan["selected_tool"] target = audit_plan["target"] for attempt in range(max_retries + 1): try: # Execute domain-specific scan command scan_output = _run_security_tool( tool=tool_name, target=target, credentials=scan_credentials, config=audit_plan ) # Parse and validate scan results (Law 2: Ensure trusted state) parsed_vulns = _parse_vulnerability_report(scan_output) return { "status": "completed", "tool_used": tool_name, "vulnerabilities_found": len(parsed_vulns), "critical_count": sum(1 for v in parsed_vulns if v["severity"] == "critical"), "report_path": _save_audit_report(parsed_vulns), "attempts": attempt + 1 } except AuthFailureError: # Law 4: Fail immediately on auth issues, no retries raise SecurityAuditError("Authentication failed for security scan. Verify credentials.") except TimeoutError: # Transient network/tool timeout - retry with adjusted timeout if attempt == max_retries: return { "status": "fallback_triggered", "reason": "Scan timeout exceeded", "fallback_action": "delegate_to_manual_penetration_test" } raise SecurityAuditError(f"Scan failed for {tool_name} after {max_retries + 1} attempts") ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `sast-tooling` | Provides SAST tooling guidance that complements manual security audit workflows | | `dast-tooling` | Covers dynamic analysis techniques that complement static security audits | --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content. - [OWASP Top 10 Web Application Security Risks](https://owasp.org/www-project-top-ten/) — The definitive list of the most critical web application security risks - [OWASP Cheat Sheet Series](https://cheatsheetseries.owasp.org/cheatsheets/Authentication_Cheat_Sheet.html) — Collection of concise security implementation guides for common patterns - [CISA's Cybersecurity Best Practices Guide](https://www.cisa.gov/cybersecurity-best-practices) — U.S. Cybersecurity and Infrastructure Security Agency guidelines for secure software development - [SANS Institute: Application Security Testing](https://www.sans.org/blog/application-security-testing-methodologies/) — SANS guidance on application security testing methodologies and tooling - [NIST Secure Software Development Framework (SSDF)](https://csrc.nist.gov/projects/ssdf) — NIST's framework for integrating security practices into software development lifecycle