---
name: auth-implementation-patterns
description: Master authentication and authorization patterns including JWT, OAuth2, session management, and RBAC to build secure, scalable access control systems. Use when implementing auth systems, securing APIs, or debugging security issues.
---

# 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](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:
```bash
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:

```bash
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