Subscription Plans: Скилл для управления подписками

--- id: SKL-subscription-SUBSCRIPTIONPLANS name: Subscription Plans description: This skill provides comprehensive patterns for managing subscription plans in billing systems. It covers tiered pricing models, per-seat pricing, usage-based pricing, plan features management, upgrade version: 1.0.0 status: active owner: '@cerebra-team' last_updated: '2026-02-22' category: Backend tags: - api - backend - server - database stack: - Python - Node.js - REST API - GraphQL difficulty: Intermediate --- # Subscription Plans ## Skill Profile *(Select at least one profile to enable specific modules)* - [ ] **DevOps** - [x] **Backend** - [ ] **Frontend** - [ ] **AI-RAG** - [ ] **Security Critical** ## Overview This skill provides comprehensive patterns for managing subscription plans in billing systems. It covers tiered pricing models, per-seat pricing, usage-based pricing, plan features management, upgrades/downgrades with proration, trial periods, cancellation handling, and migration strategies. ## Why This Matters - **Revenue Optimization**: Flexible pricing models maximize revenue - **Customer Flexibility**: Multiple plans suit different customer needs - **Competitive Advantage**: Tiered pricing attracts different market segments - **Scalability**: Usage-based pricing scales with customer growth --- ## Core Concepts & Rules ### 1. Core Principles - Follow established patterns and conventions - Maintain consistency across codebase - Document decisions and trade-offs ### 2. Implementation Guidelines - Start with the simplest viable solution - Iterate based on feedback and requirements - Test thoroughly before deployment ## Inputs / Outputs / Contracts * **Inputs**: - Environment variables: `DATABASE_URL`, `PRORATION_ENABLED`, `TRIAL_DAYS_DEFAULT` - Configuration: plan definitions, pricing models, feature flags - Database schema: plans, plan_features, subscriptions, subscription_history * **Entry Conditions**: - Database tables created for plans - Pricing models configured - Proration logic implemented * **Outputs**: - Plan records - Subscription records - Proration calculations - Trial period tracking * **Artifacts Required (Deliverables)**: - Plan management service - Pricing calculator - Proration calculator - Migration service - Trial management service * **Acceptance Evidence**: - Test coverage report (>80%) - Pricing calculation verification - Proration accuracy tests * **Success Criteria**: - Plans are created and managed correctly - Pricing calculations are accurate - Proration is fair and accurate - Trial periods work correctly ## Skill Composition * **Depends on**: [billing-cycles](../billing-cycles/), [invoice-generation](../invoice-generation/) * **Compatible with**: [usage-metering](../usage-metering/), [stripe-integration](../stripe-integration/) * **Conflicts with**: None * **Related Skills**: [subscription-plans](../subscription-plans/), [webhook-handling](../webhook-handling/) --- ## Quick Start / Implementation Example 1. Review requirements and constraints 2. Set up development environment 3. Implement core functionality following patterns 4. Write tests for critical paths 5. Run tests and fix issues 6. Document any deviations or decisions ```python # Example implementation following best practices def example_function(): # Your implementation here pass ``` ## Assumptions / Constraints / Non-goals * **Assumptions**: - Development environment is properly configured - Required dependencies are available - Team has basic understanding of domain * **Constraints**: - Must follow existing codebase conventions - Time and resource limitations - Compatibility requirements * **Non-goals**: - This skill does not cover edge cases outside scope - Not a replacement for formal training ## Compatibility & Prerequisites * **Supported Versions**: - Python 3.8+ - Node.js 16+ - Modern browsers (Chrome, Firefox, Safari, Edge) * **Required AI Tools**: - Code editor (VS Code recommended) - Testing framework appropriate for language - Version control (Git) * **Dependencies**: - Language-specific package manager - Build tools - Testing libraries * **Environment Setup**: - `.env.example` keys: `API_KEY`, `DATABASE_URL` (no values) ## Test Scenario Matrix (QA Strategy) | Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | **Unit** | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | | **Integration** | DB / API | All external API calls or database connections must be mocked during unit tests | | **E2E** | User Journey | Critical user flows to test | | **Performance** | Latency / Load | Benchmark requirements | | **Security** | Vuln / Auth | SAST/DAST or dependency audit | | **Frontend** | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) | ## Technical Guardrails & Security Threat Model ### 1. Security & Privacy (Threat Model) * **Top Threats**: Injection attacks, authentication bypass, data exposure - [ ] **Data Handling**: Sanitize all user inputs to prevent Injection attacks. Never log raw PII - [ ] **Secrets Management**: No hardcoded API keys. Use Env Vars/Secrets Manager - [ ] **Authorization**: Validate user permissions before state changes ### 2. Performance & Resources - [ ] **Execution Efficiency**: Consider time complexity for algorithms - [ ] **Memory Management**: Use streams/pagination for large data - [ ] **Resource Cleanup**: Close DB connections/file handlers in finally blocks ### 3. Architecture & Scalability - [ ] **Design Pattern**: Follow SOLID principles, use Dependency Injection - [ ] **Modularity**: Decouple logic from UI/Frameworks ### 4. Observability & Reliability - [ ] **Logging Standards**: Structured JSON, include trace IDs `request_id` - [ ] **Metrics**: Track `error_rate`, `latency`, `queue_depth` - [ ] **Error Handling**: Standardized error codes, no bare except - [ ] **Observability Artifacts**: - **Log Fields**: timestamp, level, message, request_id - **Metrics**: request_count, error_count, response_time - **Dashboards/Alerts**: High Error Rate > 5% ## Agent Directives & Error Recovery *(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)* - **Thinking Process**: Analyze root cause before fixing. Do not brute-force. - **Fallback Strategy**: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification. - **Self-Review**: Check against Guardrails & Anti-patterns before finalizing. - **Output Constraints**: Output ONLY the modified code block. Do not explain unless asked. ## Definition of Done (DoD) Checklist - [ ] Tests passed + coverage met - [ ] Lint/Typecheck passed - [ ] Logging/Metrics/Trace implemented - [ ] Security checks passed - [ ] Documentation/Changelog updated - [ ] Accessibility/Performance requirements met (if frontend) ## Anti-patterns / Pitfalls * ⛔ **Don't**: Log PII, catch-all exception, N+1 queries * ⚠️ **Watch out for**: Common symptoms and quick fixes * 💡 **Instead**: Use proper error handling, pagination, and logging ## Reference Links & Examples * Internal documentation and examples * Official documentation and best practices * Community resources and discussions ## Versioning & Changelog * **Version**: 1.0.0 * **Changelog**: - 2026-02-22: Initial version with complete template structure