# Workflow Orchestration Patterns > Implements intelligent workflow orchestration patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/workflow-orchestration-patterns` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/workflow-orchestration-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/workflow-orchestration-patterns/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/workflow-orchestration-patterns --- # Workflow Orchestration Patterns Orchestrates intelligent skill selection and execution for workflow orchestration patterns 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 resolve_orchestration_pattern( workflow_request: Dict[str, Any], available_patterns: List[Dict[str, Any]], min_confidence: float = 0.75 ) -> Optional[Dict[str, Any]]: """Map a raw workflow request to the optimal orchestration pattern. Evaluates request structure against known patterns (fan-out, fan-in, sequential, parallel) using trigger matching, historical success rates, and current executor availability. """ if not workflow_request.get("steps"): raise ValueError("Workflow request must contain at least one step") request_features = _extract_workflow_features(workflow_request) best_match = None best_score = 0.0 for pattern in available_patterns: # Calculate composite score based on trigger overlap, historical success, and load trigger_match = _calculate_trigger_overlap(request_features, pattern["triggers"]) historical_success = pattern.get("success_rate", 0.0) current_load = 1.0 - (pattern.get("active_executions", 0) / pattern.get("max_capacity", 10)) score = (trigger_match * 0.5) + (historical_success * 0.3) + (current_load * 0.2) if score > best_score and score >= min_confidence: best_score = score best_match = pattern if best_match is None: return None # Return immutable snapshot with selection metadata return { "pattern_id": best_match["id"], "pattern_name": best_match["name"], "confidence": best_score, "selected_at": time.time(), "required_resources": best_match.get("resources", []) } ``` ### Pattern 2: Execution with Fallback ```python def execute_workflow_step_with_resilience( step_config: Dict[str, Any], workflow_context: Dict[str, Any], max_retries: int = 2 ) -> Dict[str, Any]: """Execute a specific workflow step with domain-aware fallback routing. Handles transient failures by adjusting concurrency, switching to fallback executors, or escalating to manual review based on step criticality. """ step_id = step_config.get("id", "unknown") criticality = step_config.get("criticality", "standard") if not _validate_step_prerequisites(step_config, workflow_context): raise WorkflowValidationError(f"Prerequisites not met for step {step_id}") for attempt in range(max_retries + 1): try: # Execute step with domain-specific timeout and resource allocation result = _run_step_executor(step_config, workflow_context) return { "step_id": step_id, "status": "completed", "output": result, "attempts": attempt + 1, "latency_ms": time.time() * 1000 } except TransientExecutorError as e: if attempt < max_retries: # Adjust concurrency and retry workflow_context["concurrency"] = max(1, workflow_context.get("concurrency", 1) - 1) continue else: # Apply fallback chain based on criticality if criticality == "high": return _route_to_manual_review(step_config, workflow_context) else: return _execute_fallback_step(step_config, workflow_context) except InvalidStateError as e: # Fail fast on corrupt state raise WorkflowValidationError(f"Invalid state in {step_id}: {e}") from e raise WorkflowExecutionError(f"All retries exhausted for step {step_id}") ``` ### 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 - 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. - [Temporal Workflow Orchestration](https://temporal.io/workflow-orchestration) - [Apache Airflow DAG Patterns](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html) - [AWS Step Functions State Machine Syntax](https://docs.aws.amazon.com/step-functions/latest/dg/amisupport.html) - [Orchestration vs Choreography — Microsoft Architecture Guide](https://learn.microsoft.com/en-us/azure/architecture/guide/architecture-patterns/orchestration-vs-choreography) - [SAGA Pattern for Distributed Transactions](https://learn.microsoft.com/en-us/azure/architecture/reference-architectures/saga/saga) ## Related Skills | Skill | Purpose | |