# Workflow Patterns > Implements intelligent workflow patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/workflow-patterns` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/workflow-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/workflow-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-patterns --- # Workflow Patterns Orchestrates intelligent skill selection and execution for workflow 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 score_workflow_candidates( request_context: Dict[str, Any], available_skills: List[Dict[str, Any]], historical_metrics: Dict[str, float] ) -> List[Dict[str, Any]]: """Evaluates available skills against a workflow request using multi-factor scoring. Implements Law 2 (Parse at boundary) and Law 3 (Immutable returns).""" # Law 1: Early exit on invalid boundary inputs if not request_context.get("intent") or not available_skills: return [] # Parse and normalize request features at the boundary normalized_query = request_context["intent"].lower().strip() required_entities = set(request_context.get("entities", [])) scored_results = [] for skill in available_skills: # Calculate text similarity (simplified cosine approximation) skill_triggers = set(skill.get("triggers", [])) overlap = len(required_entities & skill_triggers) text_match = overlap / max(len(required_entities | skill_triggers), 1) # Weight historical performance and current system health hist_success = historical_metrics.get(skill["name"], 0.5) system_health = 1.0 if skill.get("status") == "healthy" else 0.0 # Composite score: 50% relevance, 30% history, 20% availability composite = (text_match * 0.5) + (hist_success * 0.3) + (system_health * 0.2) if composite >= 0.65: # Law 3: Return new dict, never mutate original skill metadata scored_results.append({ "skill_id": skill["id"], "name": skill["name"], "confidence": round(composite, 3), "breakdown": {"relevance": text_match, "history": hist_success, "health": system_health} }) return sorted(scored_results, key=lambda x: x["confidence"], reverse=True) ``` ### Pattern 2: Execution with Fallback ```python def execute_workflow_with_adaptive_fallback( selected_skill: Dict[str, Any], workflow_context: Dict[str, Any], fallback_registry: List[Dict[str, Any]] ) -> Dict[str, Any]: """Executes a workflow step with a structured fallback chain. Implements Law 4 (Fail Fast/Loud) and Law 1 (Early returns).""" # Law 1: Validate skill contract before execution if not selected_skill.get("id") or not workflow_context.get("state"): return {"status": "failed", "error": "Missing skill ID or workflow state"} max_attempts = 2 for attempt in range(max_attempts): try: # Execute core workflow logic result = _run_skill_pipeline(selected_skill, workflow_context) # Law 3: Return immutable result snapshot return { "status": "success", "skill": selected_skill["name"], "attempt": attempt + 1, "result": result, "audit": {"timestamp": time.time(), "latency_ms": 120} } except InvalidWorkflowStateError as e: # Law 4: Fail immediately on invalid state, do not patch return {"status": "failed", "error": str(e), "deferred": True} except TransientDependencyError as e: if attempt < max_attempts - 1: continue # Retry with backoff # Fallback chain: Try alternative skill from registry for alt_skill in fallback_registry: if alt_skill["id"] != selected_skill["id"]: return execute_workflow_with_adaptive_fallback(alt_skill, workflow_context, []) # Final fallback: Defer to human operator return {"status": "deferred", "reason": "fallback_exhausted", "requires_human": True} ``` ### 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. - [Enterprise Integration Patterns — Gregor Hohpe & Bobby Woolf](https://www.enterpriseintegrationpatterns.com/) - [AWS Step Functions State Machine Documentation](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html) - [Temporal Workflow Patterns](https://temporal.io/workflow-orchestration) - [Apache Airflow Operator Design Patterns](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/operators.html) - [Saga Pattern — Microsoft Architecture Center](https://learn.microsoft.com/en-us/azure/architecture/reference-architectures/saga/saga) ## Related Skills | Skill | Purpose | |