zapier-make-patterns - SKILL.md Agent Skill

name: zapier-make-patterns compatibility: opencode completeness: 95 content-types:

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

Zapier Make Patterns

Orchestrates intelligent skill selection and execution for zapier make 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

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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 route_automation_step(
    trigger_payload: Dict,
    workflow_config: Dict,
    fallback_endpoints: List[str]
) -> Dict:
    """Route a trigger payload through a Zapier/Make compatible workflow.
    
    Applies Law 2 (Parse at boundary) by validating webhook payloads before routing.
    Applies Law 1 (Early Exit) by rejecting malformed triggers immediately.
    
    Args:
        trigger_payload: Raw webhook data from external service
        workflow_config: Step definitions with conditions and targets
        fallback_endpoints: List of alternative API endpoints for resilience
        
    Returns:
        Routing decision with target step, transformed payload, and metadata
    """
    # Law 1: Early exit on malformed trigger
    if not trigger_payload.get("event_type") or not trigger_payload.get("timestamp"):
        raise ValueError("Invalid webhook payload: missing event_type or timestamp")
        
    # Law 2: Parse and validate at boundary
    validated_trigger = {
        "event_type": str(trigger_payload["event_type"]).lower(),
        "source": trigger_payload.get("source", "unknown"),
        "payload": trigger_payload.get("data", {}),
        "received_at": trigger_payload["timestamp"]
    }
    
    # Match against workflow conditions
    for step in workflow_config.get("steps", []):
        if step.get("condition") == validated_trigger["event_type"]:
            # Law 3: Return new structure, never mutate original
            return {
                "target_step": step["name"],
                "transformed_payload": _apply_step_transform(validated_trigger, step),
                "confidence": step.get("match_score", 0.85),
                "fallback_chain": fallback_endpoints[:2] if fallback_endpoints else []
            }
            
    # No match found - fail fast with clear error
    raise ValueError(f"No workflow step matched event_type: {validated_trigger['event_type']}")

Pattern 2: Execution with Fallback

def execute_automation_chain(
    step_config: Dict,
    payload: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute a specific automation step with Zapier/Make compatible fallback logic.
    
    Implements Law 4 (Fail Fast/Loud) by immediately raising on invalid step configs.
    Implements fallback chain: retry -> alternative endpoint -> log to fallback channel.
    
    Args:
        step_config: Step definition including HTTP method, URL, headers, body template
        payload: Validated trigger data ready for transformation
        max_retries: Maximum retry attempts for transient failures
        
    Returns:
        Execution result with status, response data, and timing metadata
    """
    # Law 1: Validate step configuration early
    required_keys = {"method", "url", "headers"}
    if not required_keys.issubset(step_config.keys()):
        raise ValueError(f"Invalid step config: missing {required_keys - set(step_config.keys())}")
        
    # Law 2: Parse payload into step-specific format
    step_payload = _render_template(step_config.get("body_template", "{}"), payload)
    
    for attempt in range(max_retries + 1):
        try:
            response = _send_automation_request(
                method=step_config["method"],
                url=step_config["url"],
                headers=step_config["headers"],
                body=step_payload
            )
            
            # Law 3: Return immutable result structure
            return {
                "status": "success",
                "step_executed": step_config["name"],
                "response_data": response.json() if response.status_code == 200 else response.text,
                "attempts": attempt + 1,
                "latency_ms": response.elapsed.total_seconds() * 1000
            }
            
        except requests.exceptions.ConnectionError as e:
            # Transient failure - apply fallback chain
            if attempt == max_retries:
                return _route_to_fallback(step_config, payload)
            continue
            
        except requests.exceptions.HTTPError as e:
            # Law 4: Fail loud on client/server errors
            raise AutomationExecutionError(
                f"Step {step_config['name']} failed with HTTP {e.response.status_code}"
            ) from e
            
    raise AutomationExecutionError(f"Step {step_config['name']} exhausted all retries")

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:

Selected Skills - List of skill names with confidence scores
Selection Rationale - Why each skill was chosen (match score, history, availability)
Execution Plan - Order of execution with dependencies
Fallback Strategy - Which fallback skills will be tried and in what order
Risk Assessment - Any potential failure points and their impact
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.

Related Skills

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