# Conductor Validator > Implements intelligent conductor validator with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/conductor-validator` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/conductor-validator` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/conductor-validator/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/conductor-validator --- # Conductor Validator Orchestrates intelligent skill selection and execution for conductor validator 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 validate_conductor_routing( task_spec: Dict[str, Any], conductor_registry: List[Dict[str, Any]], min_confidence: float = 0.75 ) -> Dict[str, Any]: """Validate and route tasks through the conductor pipeline. Applies multi-factor scoring to select the optimal conductor while enforcing the 5 Laws of Elegant Defense. """ if not task_spec.get("intent") or not conductor_registry: raise ValueError("Task intent and conductor registry are required") task_features = _extract_intent_features(task_spec["intent"]) scored_conductors = [] for conductor in conductor_registry: # Law 2: Parse at boundary - validate conductor schema if not _validate_conductor_schema(conductor): continue # Multi-factor scoring text_match = _cosine_similarity(task_features, conductor["trigger_patterns"]) historical_success = conductor.get("success_rate", 0.0) system_health = conductor.get("health_status", "unknown") # Weighted scoring algorithm raw_score = (text_match * 0.5) + (historical_success * 0.3) + (0.2 if system_health == "healthy" else 0.0) if raw_score >= min_confidence: scored_conductors.append({ "conductor_id": conductor["id"], "score": round(raw_score, 3), "dependencies": conductor.get("requires", []), "fallback_targets": conductor.get("fallback_chain", []) }) if not scored_conductors: return {"status": "no_match", "alternatives": []} # Law 3: Return new structure, never mutate registry best_match = max(scored_conductors, key=lambda x: x["score"]) return { "selected_conductor": best_match, "validation_timestamp": time.time(), "confidence": best_match["score"] } ``` ### Pattern 2: Execution with Fallback ```python def execute_conductor_with_resilience( selected_conductor: Dict[str, Any], task_payload: Dict[str, Any], fallback_registry: Dict[str, List[str]] ) -> Dict[str, Any]: """Execute conductor validation with built-in fallback chains. Implements Fail Fast, Fail Loud (Law 4) with adaptive retry logic. """ conductor_id = selected_conductor["conductor_id"] max_retries = selected_conductor.get("max_retries", 2) for attempt in range(max_retries + 1): try: # Law 1: Early exit on invalid payload if not _validate_payload_schema(task_payload, conductor_id): raise InvalidStateError(f"Payload mismatch for {conductor_id}") result = _invoke_conductor_api(conductor_id, task_payload) # Law 3: Atomic predictability - return immutable result return { "status": "success", "conductor": conductor_id, "output": result, "attempts": attempt + 1, "latency_ms": time.time() * 1000 } except InvalidStateError as e: raise SkillExecutionError(f"Validation failed at attempt {attempt + 1}: {e}") from e except TransientError as e: if attempt == max_retries: # Law 4: Fail loud - trigger fallback chain fallback_targets = fallback_registry.get(conductor_id, []) if fallback_targets: return _execute_fallback_chain(fallback_targets, task_payload) raise SkillExecutionError(f"No fallback available for {conductor_id}") from e raise SkillExecutionError(f"Conductor {conductor_id} exhausted all retries") ``` ### 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 - Parse user request into structured task specifications before dispatching to downstream agents - Implement a state machine for each conductor phase with explicit entry/exit conditions and transition logs - Validate validator outputs against expected schema before proceeding to the next orchestration step - Log every orchestration decision including rationale, selected strategy, and confidence scores for auditability - Maintain a task queue with priority ordering — critical path items execute first during resource contention ### MUST NOT DO - Do not allow a single failed agent task to silently terminate the entire workflow — implement per-step fallbacks - Avoid circular delegation patterns where Agent A delegates to B which delegates back to A without termination condition - Never bypass the validation step for validator results even if timing is critical — correctness supersedes speed - Do not use shared mutable state between parallel agent executions — use message-passing or immutable data transfer - Avoid hardcoding agent selection rules; parameterize them and load from configuration for runtime flexibility ## 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. - [Workflow Schema Validation Patterns]() - [OpenAPI Specification for API Validation]() - [JSON Schema Documentation]() - [Netflix Conductor Task Validators]() - [Input/Output Validation Frameworks (Pydantic)]() ## Related Skills | Skill | Purpose | |