🧠 AgentDB & Memory Skills
/docs/development-tools/agentdb--memory-skills
Skills are reusable workflows that combine agents, hooks, and patterns into ready-to-use solutions. Think of them as "recipes" for common development tasks.
How Skills Work
┌──────────────────────────────────────────────────────────────────┐ │ SKILL EXECUTION │ ├──────────────────────────────────────────────────────────────────┤ │ You: "Run /github-code-review" │ │ ↓ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Load Skill │──▶│ Spawn Agents│──▶│ Execute │ │ │ │ Definition │ │ (5 agents) │ │ Workflow │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ │ │ └──── Learns from outcome ─────────┘ │ └──────────────────────────────────────────────────────────────────┘
All 42 Skills by Category
Details
| Skill | What It Does | When To Use |
|---|---|---|
agentdb-vector-search | Semantic search with 150x faster retrieval | Building RAG systems, knowledge bases |
agentdb-memory-patterns | Session memory, persistent storage, context management | Stateful agents, chat systems |
agentdb-learning | 9 RL algorithms (PPO, DQN, SARSA, etc.) | Self-learning agents, behavior optimization |
agentdb-optimization | Quantization (4-32x memory reduction), HNSW indexing | Scaling to millions of vectors |
agentdb-advanced | QUIC sync, multi-database, custom distance metrics | Distributed AI systems |
bash# Example: Initialize vector search /agentdb-vector-search