Cc Skill Security Review

Cc Skill Security Review

Orchestrates intelligent skill selection and execution for cc skill security review 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 select_security_rules(
    target_language: str,
    code_context: Dict[str, Any],
    available_rules: List[SecurityRule]
) -> List[SecurityRule]:
    """Select relevant security rules based on target language and code context.
    
    Implements multi-factor scoring for rule selection:
    - Language/framework compatibility
    - Historical vulnerability density for similar codebases
    - Rule coverage vs. performance overhead
    
    Args:
        target_language: Programming language or framework identifier
        code_context: Parsed code structure and configuration metadata
        available_rules: List of available security rule definitions
        
    Returns:
        Filtered and sorted list of applicable security rules
    """
    # Law 1 & 2: Guard clauses and input validation
    if not target_language or not isinstance(target_language, str):
        raise ValueError("Target language must be a non-empty string")
    if not available_rules:
        raise ValueError("At least one security rule must be provided")
        
    selected = []
    for rule in available_rules:
        score = 0.0
        # Factor 1: Language match
        if target_language in rule.supported_languages:
            score += 0.5
        # Factor 2: Context relevance (e.g., detects auth bypass in web frameworks)
        if _matches_context(rule, code_context):
            score += 0.3
        # Factor 3: Historical effectiveness
        score += rule.historical_detection_rate * 0.2
            
        if score >= rule.min_confidence_threshold:
            selected.append(rule)
            
    # Sort by relevance score descending
    selected.sort(key=lambda r: r.historical_detection_rate, reverse=True)
    return selected

Pattern 2: Execution with Fallback

def execute_security_scan(
    target_code: str,
    selected_rules: List[SecurityRule],
    fallback_strategy: str = "dynamic_analysis"
) -> SecurityReport:
    """Execute security scan with fallback chain for resilience.
    
    Implements Fail Fast, Fail Loud (Law 4):
    - Invalid code structures halt immediately
    - Fallback to alternative analysis methods if static analysis fails
    
    Args:
        target_code: Source code or configuration to analyze
        selected_rules: Pre-filtered security rules to apply
        fallback_strategy: Method to use if primary analysis fails
        
    Returns:
        Immutable SecurityReport with findings and risk assessment
    """
    if not target_code or not selected_rules:
        raise ValueError("Code and rules are required for scanning")
        
    findings = []
    scan_attempts = 0
    
    try:
        # Primary: Static Analysis
        static_results = _run_static_analysis(target_code, selected_rules)
        findings.extend(static_results)
        scan_attempts += 1
        
    except ParseError as e:
        # Fallback 1: Dynamic Analysis / Runtime Inspection
        if fallback_strategy == "dynamic_analysis":
            findings.extend(_run_dynamic_analysis(target_code, selected_rules))
            scan_attempts += 1
        else:
            raise ScanExecutionError(f"Static analysis failed: {e}") from e
            
    except TimeoutError:
        # Fallback 2: Rule subset reduction & retry
        findings.extend(_run_reduced_rule_scan(target_code, selected_rules[:5]))
        scan_attempts += 1
        
    # Law 3: Return new data structure, never mutate inputs
    report = SecurityReport(
        findings=findings,
        scan_attempts=scan_attempts,
        status="CRITICAL" if any(f.severity == "HIGH" for f in findings) else "PASS"
    )
    
    # Law 4: Fail loud on critical vulnerabilities
    if report.status == "CRITICAL":
        raise CriticalSecurityViolation(report)
        
    return report

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

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 skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• OWASP Top 10 Web Application Risks
• OWASP Testing Guide v4
• CWE/SANS Top 25 Most Dangerous Software Errors
• NIST Cybersecurity Framework
• SAST vs DAST Comparison (OWASP)

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