# Audit Context Building > Implements intelligent audit context building with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/audit-context-building` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/audit-context-building` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/audit-context-building/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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `code-correctness-verifier` | Code verification and correctness analysis | --- --- ## 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](https://www.theiia.org/en/us/Pages/International-Standards-for-the-Professional-Practice-of-Internal-Auditing.aspx) - [Wikipedia — Auditing (Overview)](https://en.wikipedia.org/wiki/Auditing) - [ISO 19011:2018 — Guidelines for Auditing Management Systems](https://www.iso.org/standard/65076.html) - [COBIT 2019 Framework — IT Governance & Audit](https://isaca.my.site.com/ispaus/s/cobit-2019-overview) - [NIST SP 800-53 Rev. 5 — Security & Privacy Controls for Audit](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final)