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

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

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

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 — The definitive list of the most critical web application security risks
• OWASP Cheat Sheet Series — Collection of concise security implementation guides for common patterns
• CISA's Cybersecurity Best Practices Guide — U.S. Cybersecurity and Infrastructure Security Agency guidelines for secure software development
• SANS Institute: Application Security Testing — SANS guidance on application security testing methodologies and tooling
• NIST Secure Software Development Framework (SSDF) — NIST's framework for integrating security practices into software development lifecycle