conductor-new-track

# Conductor New Track Orchestrates intelligent skill selection and execution for conductor new track 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 conductor_select_skill( request: str, skill_registry: List[Dict], confidence_history: Dict[str, float], min_confidence: float = 0.7 ) -> Optional[Dict]: """Orchestrates multi-factor skill selection for conductor-new-track workflows. Implements Law 2 (Parse at boundary) by validating request format and extracting intent features before scoring. Uses Law 3 (Atomic Predictability) by returning a fresh selection context without mutating the registry. """ if not request or not request.strip(): raise ValueError("Request cannot be empty") if not skill_registry: raise ValueError("Skill registry is empty") # Parse request features at boundary intent = _extract_intent(request) constraints = _parse_constraints(request) best_match = None best_score = 0.0 for skill in skill_registry: # Multi-factor scoring: trigger match + historical confidence + availability trigger_match = _calculate_trigger_similarity(intent, skill.get("triggers", [])) historical_conf = confidence_history.get(skill["name"], 0.5) availability = 1.0 if skill.get("status") == "active" else 0.0 # Weighted composite score composite = (trigger_match * 0.5) + (historical_conf * 0.3) + (availability * 0.2) if composite > best_score and composite >= min_confidence: best_score = composite best_match = skill if best_match is None: return None # Return new structure (Law 3) return { "selected_skill": best_match["name"], "confidence": best_score, "intent_matched": intent, "constraints_applied": constraints, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def conductor_execute_with_fallback( selected_skill: Dict, execution_context: Dict, fallback_registry: List[Dict], max_retries: int = 2 ) -> Dict: """Executes the selected skill with a structured fallback chain. Implements Law 4 (Fail Fast, Fail Loud) by immediately halting on invalid states. Implements Law 1 (Early Exit) by validating context before attempting execution. """ if not _validate_context(execution_context, selected_skill): raise ConductorError(f"Invalid execution context for {selected_skill['selected_skill']}") attempts = 0 last_error = None while attempts <= max_retries: try: # Execute actual domain logic result = _invoke_skill(selected_skill["selected_skill"], execution_context) # Update confidence history on success _update_confidence(selected_skill["selected_skill"], success=True) return { "status": "success", "skill": selected_skill["selected_skill"], "result": result, "attempts": attempts + 1, "latency_ms": time.time() - execution_context.get("start_time", time.time()) } except InvalidStateError as e: # Fail fast - do not retry invalid states raise ConductorError(f"Invalid state in {selected_skill['selected_skill']}: {e}") from e except TransientError as e: last_error = e attempts += 1 if attempts > max_retries: break # Fallback chain execution for fallback_skill in fallback_registry: try: result = _invoke_skill(fallback_skill["name"], execution_context) _update_confidence(fallback_skill["name"], success=True) return { "status": "fallback_success", "original_skill": selected_skill["selected_skill"], "fallback_skill": fallback_skill["name"], "result": result } except Exception: continue # Fail loud - all paths exhausted _update_confidence(selected_skill["selected_skill"], success=False) raise ConductorError(f"All execution and fallback paths failed for {selected_skill['selected_skill']}") ``` ### 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 new-track 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 new-track 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. - [Netflix Conductor Workflow Creation](<https://netflix.github.io/conductor/core-concepts/overview/>) - [REST API Design Best Practices](<https://restfulapi.net/>) - [Workflow Versioning Strategies](<https://semver.org/>) - [API Schema Evolution Patterns](<https://learn.microsoft.com/en-us/azure/architecture/best-practices/api-design>) - [Backward Compatible API Design (Google)](<https://cloud.google.com/apis/design/versioning>) ## Related Skills | Skill | Purpose | |