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

• guidance
• examples
• do-dont description: Implements intelligent workflow orchestration 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-orchestration-patterns, workflow orchestration patterns, how do i workflow-orchestration-patterns, orchestrate workflow-orchestration-patterns, automate workflow-orchestration-patterns, agent workflow-orchestration-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 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

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

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

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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.

• Temporal Workflow Orchestration
• Apache Airflow DAG Patterns
• AWS Step Functions State Machine Syntax
• Orchestration vs Choreography — Microsoft Architecture Guide
• SAGA Pattern for Distributed Transactions

Related Skills

| Skill | Purpose | |