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".
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。