---

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

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

---

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

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

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

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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 | |