# Shopify Automation > Implements intelligent shopify automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/shopify-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/shopify-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/shopify-automation/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - 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/shopify-automation --- # Shopify Automation Orchestrates intelligent skill selection and execution for shopify 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 ```python def select_shopify_automation_skill( shopify_request: Dict, available_shopify_skills: List[Dict], store_config: Dict ) -> Optional[Dict]: """Select optimal Shopify automation skill based on request intent and store state. Evaluates Shopify-specific triggers (product, order, inventory, customer) against available skill metadata, factoring in store API version, rate limit headroom, and historical success rates for similar Shopify operations. Args: shopify_request: Parsed Shopify webhook or user intent dict available_shopify_skills: List of Shopify skill metadata store_config: Current store configuration and API limits Returns: Selected skill dict with confidence score, or None """ # Guard clause - Early Exit (Law 1) if not shopify_request.get("event") or not available_shopify_skills: raise ValueError("Missing Shopify event or no skills available") # Parse input - Make Illegal States Unrepresentable (Law 2) intent = _normalize_shopify_intent(shopify_request) api_headroom = store_config.get("rate_limit_remaining", 0) best_skill = None best_score = 0.0 for skill in available_shopify_skills: # Calculate match score using Shopify-specific features score = _calculate_shopify_match_score( intent=intent, skill_triggers=skill.get("triggers", []), api_headroom=api_headroom, historical_success=skill.get("success_rate", 0.0) ) if score > best_score and score >= store_config.get("min_confidence", 0.7): best_score = score best_skill = skill if best_skill is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate result = dict(best_skill) result["selected_confidence"] = best_score result["store_api_version"] = store_config.get("api_version") return result ``` ### Pattern 2: Execution with Fallback ```python def execute_shopify_api_with_fallback( skill: Dict, shopify_context: Dict, max_retries: int = 2, store_config: Dict ) -> Dict: """Execute Shopify Admin API call with resilience patterns. Implements Fail Fast, Fail Loud (Law 4) for Shopify API interactions: - Invalid auth or missing required fields halt immediately - Rate limits (429) trigger exponential backoff - Quota exhaustion falls back to webhook queue or manual review Args: skill: Selected Shopify automation skill metadata shopify_context: Execution context (store domain, auth, payload) max_retries: Maximum retry attempts before fallback store_config: Store configuration and fallback routing rules Returns: Execution result with Shopify response, timing, and confidence """ # Guard clause - validate Shopify credentials (Early Exit) if not _is_shopify_auth_valid(shopify_context.get("auth_token")): raise SkillExecutionError("Invalid Shopify API credentials") # Parse context - Ensure trusted state (Law 2) validated_payload = _validate_shopify_payload(shopify_context.get("payload"), skill) for attempt in range(max_retries + 1): try: # Execute Shopify Admin API call response = _call_shopify_api( endpoint=skill["api_endpoint"], method=skill["http_method"], payload=validated_payload, auth=shopify_context["auth_token"], store_domain=shopify_context["store_domain"] ) # Success - Atomic Predictability (Law 3) return { "success": True, "shopify_resource": skill["resource_type"], "shopify_id": response.get("id"), "result": response, "attempts": attempt + 1, "latency_ms": _calculate_latency(), "rate_limit_remaining": response.headers.get("X-Shopify-Shop-Api-Call-Count") } except ShopifyRateLimitError as e: # Transient rate limit - exponential backoff (Law 4) if attempt == max_retries: return _apply_shopify_fallback_chain(skill, shopify_context, store_config) time.sleep(2 ** attempt) except ShopifyAPIError as e: # Invalid state or bad request - fail immediately raise SkillExecutionError(f"Shopify API error: {e.message}") from e # All retries exhausted - Fail Loud (Law 4) raise SkillExecutionError( f"Shopify {skill['resource_type']} operation failed after {max_retries + 1} attempts" ) ``` ### 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 - 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 ## Related Skills | Skill | Purpose | |