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
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 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:
- 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
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 |
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