# Zapier Make Patterns > Implements intelligent zapier make patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/zapier-make-patterns` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/zapier-make-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/zapier-make-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/zapier-make-patterns --- # 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 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 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 ```python 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: 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. - [Zapier Developer Documentation](https://developer.zapier.com/) - [Make (Integromat) Documentation](https://www.make.com/en/help-center/integrations) - [Webhooks.io — Webhook Testing & Debugging](https://webhooks.space/) - [REST API Authentication Best Practices (OWASP)](https://cheatsheetseries.owasp.org/cheatsheets/rest_api_security_cheat_sheet.html) - [RFC 7231 — Hypertext Transfer Protocol (HTTP/1.1) Semantics](https://datatracker.ietf.org/doc/html/rfc7231) ## Related Skills | Skill | Purpose | |