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name: workflow-patterns compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent workflow patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: workflow-patterns, workflow patterns, how do i workflow-patterns, orchestrate workflow-patterns, automate workflow-patterns, agent workflow-patterns archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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
• AWS Step Functions State Machine Documentation
• Temporal Workflow Patterns
• Apache Airflow Operator Design Patterns
• Saga Pattern — Microsoft Architecture Center

Related Skills

| Skill | Purpose | |