---

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

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

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Stripe Automation

Orchestrates intelligent skill selection and execution for stripe 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 route_stripe_intent(
    user_request: Dict,
    stripe_config: Dict,
    min_confidence: float = 0.7
) -> Dict:
    """Route a Stripe automation request to the appropriate SDK endpoint.
    
    Validates parameters against Stripe API requirements and selects
    the optimal operation (customer, payment, subscription, webhook).
    """
    if not user_request.get("intent") or not stripe_config.get("api_key"):
        raise ValueError("Missing required Stripe intent or API configuration")
        
    intent = user_request["intent"].lower()
    params = user_request.get("parameters", {})
    
    # Map intents to Stripe SDK methods with parameter validation
    stripe_routes = {
        "create_customer": ("customers", "create", {"email": str, "name": str}),
        "process_payment": ("charges", "create", {"amount": int, "currency": str, "source": str}),
        "update_subscription": ("subscriptions", "update", {"subscription_id": str, "status": str}),
        "handle_webhook": ("webhooks", "construct", {"payload": str, "sig_header": str})
    }
    
    if intent not in stripe_routes:
        raise ValueError(f"Unsupported Stripe intent: {intent}")
        
    endpoint, method, required_fields = stripe_routes[intent]
    missing = [f for f in required_fields if f not in params]
    if missing:
        raise ValueError(f"Missing required Stripe parameters: {missing}")
        
    return {
        "route": f"stripe.{endpoint}.{method}",
        "validated_params": params,
        "intent": intent,
        "confidence": min_confidence,
        "idempotency_key": f"stripe_{intent}_{uuid4()}"
    }

Pattern 2: Execution with Fallback

def execute_stripe_operation(
    route_config: Dict,
    stripe_client: Any,
    max_retries: int = 2
) -> Dict:
    """Execute a validated Stripe operation with idempotency and fallback handling.
    
    Implements Stripe-specific resilience:
    - Idempotency keys prevent duplicate charges/subscriptions
    - Handles StripeCardError, StripeAPIError, and rate limits
    - Falls back to manual review or alternative payment methods
    """
    endpoint = route_config["route"]
    params = route_config["validated_params"]
    idempotency_key = route_config["idempotency_key"]
    
    for attempt in range(max_retries + 1):
        try:
            # Parse endpoint string to dynamic method call
            module_name, method_name = endpoint.split(".")
            stripe_module = getattr(stripe, module_name)
            method = getattr(stripe_module, method_name)
            
            # Execute with idempotency key for safe retries
            result = method(
                **params,
                idempotency_key=idempotency_key if method_name != "construct" else None
            )
            
            return {
                "success": True,
                "stripe_operation": endpoint,
                "stripe_id": result.get("id"),
                "status": result.get("status"),
                "attempts": attempt + 1,
                "latency_ms": time.time() * 1000
            }
            
        except stripe.error.CardError as e:
            # Payment failed - immediate fail, no retry for card errors
            raise StripeOperationError(
                f"Card declined: {e.user_message}",
                stripe_code=e.code
            ) from e
            
        except stripe.error.RateLimitError as e:
            # Transient - retry with exponential backoff
            if attempt < max_retries:
                time.sleep(2 ** attempt)
                continue
            return _fallback_to_manual_review(route_config, e)
            
        except stripe.error.APIError as e:
            # Other API errors - fallback chain
            if attempt == max_retries:
                return _fallback_to_alternative_payment(route_config, e)
                
    raise StripeOperationError(f"Stripe {endpoint} 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

Related Skills

Skill	Purpose
sendgrid-automation	Email automation counterpart — Stripe events trigger SendGrid notifications for payment workflows
slack-automation	Slack notifications for Stripe billing events and subscription changes

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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

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• Stripe API Documentation — Official Stripe API reference covering payments, subscriptions, webhooks, and billing
• Stripe CLI Documentation — Stripe CLI reference for local webhook development and testing
• Stripe Webhook Best Practices — Official Stripe guide on designing robust webhook handlers for payment events
• Stripe Python Library (GitHub) — Official Stripe Python SDK source code with usage examples
• Payment Automation Architecture Patterns (PCI DSS) — PCI DSS standards for secure payment processing and automation