Promote a validated pattern into a reusable Skill in AgentDB's skill library. Use when the same approach has worked 3+ times across episodes, or when the user explicitly says "make this a skill" / "save this as reusable".
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Take an approach that's been validated multiple times and elevate it to a first-class Skill — queryable by intent, composable into chains, and tracked separately from one-off patterns.
agentdb_skill_create(
name: <verb-noun-noun> // e.g. "rotate-jwt-refresh-token"
description: <one-liner> // what it does
precondition: <plain English query> // matched by intent embedding
action: <step list> // executable steps
outcome: <success criteria> // what "worked" looks like
tags: [<topic>, <stack>, ...]
metadata: { sourceEpisodeIds: [...], confidence }
)
The precondition is what makes the skill discoverable — it's embedded and matched against future task intents. Write it like a search query: "need to rotate a JWT refresh token without invalidating active sessions", not like a docstring.
Skills can chain via agentdb_skill_compose:
compose(
intent: "set up authenticated API with token rotation",
available: [skill_a, skill_b, skill_c]
) → bandit picks the best chain ordering
The bandit tracks composition rewards over time; bad orderings decay automatically.
action. Use placeholders.No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
The English summary is being prepared. Showing the original description.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
The English summary is being prepared. Showing the original description.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
The English summary is being prepared. Showing the original description.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
The English summary is being prepared. Showing the original description.
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
The English summary is being prepared. Showing the original description.
Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
The English summary is being prepared. Showing the original description.