Auth Implementation Patterns

Authentication & Authorization Implementation Patterns

Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices.

Use this skill when

• Implementing user authentication systems
• Securing REST or GraphQL APIs
• Adding OAuth2/social login or SSO
• Designing session management or RBAC
• Debugging authentication or authorization issues

Do not use this skill when

• You only need UI copy or login page styling
• The task is infrastructure-only without identity concerns
• You cannot change auth policies or credential storage

Instructions

• Define users, tenants, flows, and threat model constraints.
• Choose auth strategy (session, JWT, OIDC) and token lifecycle.
• Design authorization model and policy enforcement points.
• Plan secrets storage, rotation, logging, and audit requirements.
• If detailed examples are required, open resources/implementation-playbook.md.

Safety

• Never log secrets, tokens, or credentials.
• Enforce least privilege and secure storage for keys.

Resources

• resources/implementation-playbook.md for detailed patterns and examples.

🧠 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 auth-implementation-patterns <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