// API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
| name | api-patterns |
| description | API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination. |
| when_to_use | When designing REST/GraphQL/tRPC APIs, defining response formats, versioning, pagination, or API authentication. NOT for UI/frontend work. |
| allowed-tools | Read, Write, Edit, Glob, Grep |
API design principles and decision-making for 2025. Learn to THINK, not copy fixed patterns.
Read ONLY files relevant to the request! Check the content map, find what you need.
| File | Description | When to Read |
|---|---|---|
api-style.md | REST vs GraphQL vs tRPC decision tree | Choosing API type |
rest.md | Resource naming, HTTP methods, status codes | Designing REST API |
response.md | Envelope pattern, error format, pagination | Response structure |
graphql.md | Schema design, when to use, security | Considering GraphQL |
trpc.md | TypeScript monorepo, type safety | TS fullstack projects |
versioning.md | URI/Header/Query versioning | API evolution planning |
auth.md | JWT, OAuth, Passkey, API Keys | Auth pattern selection |
rate-limiting.md | Token bucket, sliding window | API protection |
documentation.md | OpenAPI/Swagger best practices | Documentation |
security-testing.md | OWASP API Top 10, auth/authz testing | Security audits |
| Need | Skill |
|---|---|
| API implementation | @[skills/backend-development] |
| Data structure | @[skills/database-design] |
| Security details | @[skills/security-hardening] |
Before designing an API:
DON'T:
DO:
| Script | Purpose | Command |
|---|---|---|
scripts/api_validator.py | API endpoint validation | python scripts/api_validator.py <project_path> |
Socratic questioning protocol + user communication. MANDATORY for complex requests, new features, or unclear requirements. Includes progress reporting and error handling.
Token-efficient code review using Tree-sitter AST graphs and MCP. Reduces AI assistant token usage by 6.8–49x by computing blast radius of changes instead of reading entire codebases. Uses SQLite graph database for structural analysis.
Persistent cross-session memory management. Enables agents to remember user preferences, project conventions, and past decisions across different sessions using a structured MEMORY.md index and topic files.
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
Reduce complexity of over-engineered code. Identify unnecessary abstractions, remove dead code, flatten deep nesting, and simplify logic while preserving behavior.
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
