---

name: make-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent make automation 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: make-automation, make automation, how do i make-automation, orchestrate make-automation, automate make-automation, agent make-automation 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"

---

Make Automation

Orchestrates intelligent skill selection and execution for make automation 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_make_automation_trigger(
    request_payload: Dict[str, Any],
    registered_automations: List[Dict[str, Any]],
    min_trigger_match: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Resolve the optimal Make.com automation blueprint for a given request.
    
    Matches incoming payloads against registered automation triggers using
    event-type compatibility, historical success rates, and dependency health.
    """
    if not request_payload or not registered_automations:
        raise ValueError("Request payload and registered automations are required")
        
    trigger_type = request_payload.get("trigger_type", "webhook")
    event_data = request_payload.get("event", {})
    
    best_automation = None
    best_score = 0.0
    
    for automation in registered_automations:
        # Calculate trigger compatibility score
        trigger_match = _calculate_trigger_compatibility(trigger_type, automation["triggers"])
        history_score = automation.get("success_rate", 0.0) * 0.4
        dependency_health = _check_dependency_health(automation.get("dependencies", []))
        
        composite_score = (trigger_match * 0.5) + (history_score * 0.3) + (dependency_health * 0.2)
        
        if composite_score > best_score and composite_score >= min_trigger_match:
            best_score = composite_score
            best_automation = automation
            
    if best_automation is None:
        return None
        
    # Return immutable snapshot with execution metadata
    return {
        "automation_id": best_automation["id"],
        "trigger_config": best_automation["triggers"],
        "estimated_steps": len(best_automation["steps"]),
        "confidence": best_score,
        "resolved_at": time.time()
    }

Pattern 2: Execution with Fallback

def run_make_automation_pipeline(
    automation_blueprint: Dict[str, Any],
    execution_context: Dict[str, Any],
    max_step_retries: int = 3
) -> Dict[str, Any]:
    """Execute a resolved Make automation pipeline with domain-specific fallback routing.
    
    Handles step-by-step execution, transient API failures, and fallback to 
    alternative automation routes or manual intervention when critical steps fail.
    """
    steps = automation_blueprint.get("steps", [])
    if not steps:
        raise ValueError("Automation blueprint contains no executable steps")
        
    execution_log = []
    current_context = dict(execution_context)
    
    for step_idx, step in enumerate(steps):
        step_name = step.get("name", f"step_{step_idx}")
        attempt = 0
        step_success = False
        
        while attempt < max_step_retries:
            try:
                # Execute step with domain-specific validation
                result = _execute_automation_step(step, current_context)
                current_context = _merge_step_output(current_context, result)
                step_success = True
                execution_log.append({"step": step_name, "status": "success", "attempt": attempt})
                break
                
            except RateLimitError:
                attempt += 1
                if attempt < max_step_retries:
                    time.sleep(2 ** attempt)  # Exponential backoff
                continue
                
            except CriticalDependencyError:
                # Fallback: Route to alternative automation or manual review
                fallback_route = _resolve_fallback_route(automation_blueprint, step)
                if fallback_route:
                    current_context = _execute_fallback_step(fallback_route, current_context)
                    execution_log.append({"step": step_name, "status": "fallback", "route": fallback_route["id"]})
                    step_success = True
                    break
                else:
                    raise PipelineExecutionError(f"Critical failure in {step_name} with no fallback")
                    
        if not step_success:
            raise PipelineExecutionError(f"Step {step_name} exhausted retries")
            
    # Update confidence based on execution metrics
    success_rate = len([l for l in execution_log if l["status"] == "success"]) / len(steps)
    return {
        "pipeline_id": automation_blueprint["automation_id"],
        "steps_executed": len(execution_log),
        "final_context": current_context,
        "confidence_delta": success_rate - automation_blueprint.get("confidence", 0.0),
        "execution_timestamp": time.time()
    }

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

Related Skills

| Skill | Purpose | |

---

---

Constraints

MUST DO

• Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions
• Validate all trigger conditions with explicit allowlists before executing automated actions
• Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability
• Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging

MUST NOT DO

• Do not create circular automation loops where trigger A causes action B which triggers A again
• Avoid using automations that modify production data without explicit human approval gates
• Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation
• Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues

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.

• Make (Integromat) Documentation
• Make Scenario Builder Guide
• Make Modules and Operations Reference
• Zapier vs Make Automation Comparison
• API Integration Patterns with Make