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Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?"
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
Record a causal relationship between two memories in AgentDB — "X caused Y", "A supersedes B", "patch-foo depends-on patch-bar". Use when the user is documenting cause/effect, dependencies, supersessions, or after-action analysis.
Store memories in tier-aware hierarchical memory — working / short-term / long-term — and recall with tier filters. Use for working-set context that should fade, vs facts that should persist, vs patterns that should be searchable forever.
Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".
Show AgentDB health — pattern count, embedder status, cache hit rate, learning gain since init. Use when the user asks "is agentdb working?", "how many memories?", "show agentdb stats", or after long-running sessions to confirm state.
Execute Cypher queries against AgentDB's graph backend. Use when the user wants to write a custom traversal that the standard tools don't cover, or when explaining graph state.
| name | agentdb-recall |
| description | Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?" |
Pull relevant past memories into the current context.
| Need | Tool | Returns |
|---|---|---|
| Similar past tasks | agentdb_reflexion_recall | Episodes, top-k by similarity |
| Lessons from past failures | agentdb_critique_summary | Combined critique text |
| What worked last time | agentdb_success_strategies | Approach summaries from high-reward episodes |
| Generic patterns / facts | agentdb_pattern_search | Patterns ranked by similarity |
| Reusable skills by intent | agentdb_skill_search | Skills ranked by precondition match |
agentdb_reflexion_recall with k=5, minReward=0.5.agentdb_pattern_search with the same query, k=5.agentdb_record_feedback (or recordFeedback via the library) so the bandit learns.minReward — drop low-quality matches (default 0.3).onlyFailures — explicitly query the postmortem set when debugging.onlySuccesses — only winning approaches when copying a strategy.timeWindowDays — recent context only when the codebase has shifted.