paulpas 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
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 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:
- 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
- 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 | |