conductor-status

# Conductor Status Orchestrates intelligent skill selection and execution for conductor status 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 evaluate_conductor_status_and_route( task_payload: Dict, conductor_metrics: Dict, available_skills: List[Dict], min_confidence: float = 0.75 ) -> Optional[Dict]: """Route tasks through the conductor based on real-time status metrics. Evaluates conductor health, queue depth, and skill availability to determine the optimal execution path. Implements multi-factor scoring specific to conductor orchestration workflows. """ # Law 1: Early exit on invalid conductor state if not conductor_metrics.get("heartbeat_active"): raise ConductorStatusError("Conductor heartbeat missing or stale") # Law 2: Parse and validate task against conductor constraints task_constraints = _parse_task_constraints(task_payload) if task_constraints.requires_gpu and not conductor_metrics.get("gpu_available"): return None # Skip to fallback chain best_route = None best_score = 0.0 for skill in available_skills: # Domain-specific scoring: weight by conductor load, skill latency, and historical success load_penalty = conductor_metrics.get("cpu_utilization", 0.0) * 0.3 latency_score = 1.0 / (skill.get("avg_latency_ms", 100) / 100.0) history_score = skill.get("success_rate_7d", 0.0) raw_score = (latency_score * 0.4) + (history_score * 0.4) + ((1.0 - load_penalty) * 0.2) if raw_score > best_score and raw_score >= min_confidence: best_score = raw_score best_route = { "skill_id": skill["id"], "conductor_node": conductor_metrics.get("primary_node"), "estimated_latency_ms": skill.get("avg_latency_ms"), "confidence": raw_score } if best_route is None: return None # Law 3: Return immutable route configuration return dict(best_route) ``` ### Pattern 2: Execution with Fallback ```python def execute_conductor_workflow_with_degradation( route_config: Dict, task_context: Dict, fallback_nodes: List[str] = None ) -> Dict: """Execute conductor workflow with built-in degradation and fallback logic. Monitors execution telemetry and applies domain-specific fallback strategies when conductor status degrades or task execution fails. """ primary_node = route_config.get("conductor_node") fallback_nodes = fallback_nodes or [] for attempt in range(3): try: # Law 4: Fail fast on invalid task context validated_context = _validate_task_context(task_context) # Execute against primary conductor node telemetry = _send_to_conductor(primary_node, validated_context) # Check conductor response health if telemetry.get("status") != "completed": raise ConductorDegradationError(f"Conductor returned status: {telemetry.get('status')}") return { "success": True, "node_used": primary_node, "telemetry": telemetry, "attempts": attempt + 1 } except ConductorDegradationError as e: # Law 4: Fail loud, don't patch degraded state if attempt == 2: # Fallback chain: switch to standby node or throttle if fallback_nodes: standby = fallback_nodes[0] _log_fallback_trigger(primary_node, standby) primary_node = standby continue else: raise ConductorExecutionError( f"All fallback nodes exhausted for task {task_context.get('id')}" ) from e except TransientNetworkError: # Transient issue - retry with exponential backoff time.sleep(0.5 * (2 ** attempt)) continue # Should not reach here, but enforce Law 4 raise ConductorExecutionError("Workflow execution failed after 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 status 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 status 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 REST API Reference](<https://netflix.github.io/conductor/server/rest-api/>) - [Prometheus Querying Guide (PromQL)](<https://prometheus.io/docs/prometheus/latest/querying/examples/>) - [OpenTelemetry Status Collection](<https://opentelemetry.io/docs/specs/otel/overview/>) - [Health Check Patterns in Microservices](<https://learn.microsoft.com/en-us/azure/architecture/guide/design-principles/health-check-pattern>) - [Monitoring Dashboards Best Practices (Grafana)](<https://grafana.com/docs/grafana/latest/dashboards/>) ## Related Skills | Skill | Purpose | |