API Design Principles

API Design Principles

Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.

Use this skill when

• Designing new REST or GraphQL APIs
• Refactoring existing APIs for better usability
• Establishing API design standards for your team
• Reviewing API specifications before implementation
• Migrating between API paradigms (REST to GraphQL, etc.)
• Creating developer-friendly API documentation
• Optimizing APIs for specific use cases (mobile, third-party integrations)

Do not use this skill when

• You only need implementation guidance for a specific framework
• You are doing infrastructure-only work without API contracts
• You cannot change or version public interfaces

Instructions

1. Define consumers, use cases, and constraints.
2. Choose API style and model resources or types.
3. Specify errors, versioning, pagination, and auth strategy.
4. Validate with examples and review for consistency.

Refer to resources/​implementation-playbook.md for detailed patterns, checklists, and templates.

Resources

• resources/​implementation-playbook.md for detailed patterns, checklists, and templates.

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🧠 AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/​memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/​memory_manager.py store \
  --content "Description of what was decided/​solved" \
  --type decision \
  --tags api-design-principles <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns