Audit Context Building

Audit Context Building

Orchestrates intelligent skill selection and execution for audit context building 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_audit_context(
    request: Dict,
    available_audit_skills: List[Dict],
    compliance_frameworks: List[str] = ["SOC2", "ISO27001", "GDPR"]
) -> Dict:
    """Build structured audit context by scoring and routing to relevant compliance skills.
    
    Applies Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable)
    by validating request structure and framework availability before scoring.
    """
    if not request.get("target_system") or not request.get("audit_scope"):
        raise ValueError("Audit context requires target_system and audit_scope")
        
    if not available_audit_skills:
        raise ValueError("No audit skills registered in registry")
        
    # Extract audit features (Law 2)
    scope_features = _extract_scope_features(request["audit_scope"])
    system_metadata = _fetch_system_metadata(request["target_system"])
    
    scored_candidates = []
    for skill in available_audit_skills:
        # Multi-factor scoring: framework match, historical accuracy, system compatibility
        framework_match = 1.0 if any(f in skill.get("frameworks", []) for f in compliance_frameworks) else 0.0
        historical_accuracy = skill.get("success_rate", 0.5)
        system_compat = 1.0 if system_metadata.get("type") in skill.get("supported_systems", []) else 0.0
        
        weighted_score = (framework_match * 0.4) + (historical_accuracy * 0.4) + (system_compat * 0.2)
        
        if weighted_score >= 0.6:
            scored_candidates.append({
                "skill_id": skill["id"],
                "score": weighted_score,
                "frameworks": skill["frameworks"],
                "estimated_latency_ms": skill.get("latency_ms", 500)
            })
            
    # Law 3: Return new structure, never mutate input
    audit_context = {
        "request_id": request.get("id", str(uuid.uuid4())),
        "target_system": request["target_system"],
        "scope": request["audit_scope"],
        "candidates": sorted(scored_candidates, key=lambda x: x["score"], reverse=True),
        "timestamp": time.time(),
        "frameworks_applied": compliance_frameworks
    }
    return audit_context

Pattern 2: Execution with Fallback

def execute_audit_workflow_with_fallback(
    audit_context: Dict,
    skill_registry: Dict,
    fallback_handlers: Dict[str, Callable]
) -> Dict:
    """Execute audit skill chain with resilience patterns and full audit trail.
    
    Implements Law 4 (Fail Fast, Fail Loud) and Law 5 (Audit Trail) by
    logging every state transition, handling transient compliance check failures,
    and routing to fallback handlers when primary audit steps fail.
    """
    execution_log = []
    results = {}
    
    for candidate in audit_context["candidates"]:
        skill_id = candidate["skill_id"]
        skill = skill_registry.get(skill_id)
        
        if not skill:
            execution_log.append({"step": skill_id, "status": "MISSING", "error": "Skill not in registry"})
            continue
            
        try:
            # Execute primary audit step
            raw_result = skill["handler"](audit_context["target_system"], audit_context["scope"])
            
            # Validate result structure (Law 4)
            if not _validate_audit_result(raw_result):
                raise AuditValidationError(f"Invalid compliance data from {skill_id}")
                
            results[skill_id] = {
                "status": "SUCCESS",
                "data": raw_result,
                "confidence": candidate["score"],
                "latency_ms": raw_result.get("execution_time_ms", 0)
            }
            execution_log.append({"step": skill_id, "status": "SUCCESS", "timestamp": time.time()})
            
        except TransientNetworkError:
            # Fallback chain: retry -> alternative skill -> manual review
            execution_log.append({"step": skill_id, "status": "RETRYING", "timestamp": time.time()})
            try:
                alt_result = fallback_handlers.get("retry", lambda *a, **k: None)(skill_id, audit_context)
                results[skill_id] = {"status": "FALLBACK_RETRY", "data": alt_result}
            except Exception as e:
                results[skill_id] = {"status": "MANUAL_REVIEW", "error": str(e)}
                execution_log.append({"step": skill_id, "status": "MANUAL_REVIEW", "error": str(e)})
                
    # Law 5: Compile final audit context with full trail
    return {
        "audit_context_id": audit_context["request_id"],
        "executed_skills": results,
        "execution_log": execution_log,
        "overall_confidence": sum(r.get("confidence", 0) for r in results.values()) / max(len(results), 1),
        "generated_at": time.time()
    }

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

• IIA International Standards for the Professional Practice of Internal Auditing
• Wikipedia — Auditing (Overview)
• ISO 19011:2018 — Guidelines for Auditing Management Systems
• COBIT 2019 Framework — IT Governance & Audit
• NIST SP 800-53 Rev. 5 — Security & Privacy Controls for Audit