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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".
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
| name | agentdb-init |
| description | 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". |
Sets up a fresh AgentDB instance for the current project. Creates a single-file .rvf Cognitive Container that holds vectors, indexes, learning state, and the audit log.
./memory.rvf at the project root, or ~/.agentdb/<project-name>.rvf for global memory).Xenova/all-MiniLM-L6-v2 at 384d — fast, free, runs in-process).claude mcp add agentdb -- npx agentdb@latest mcp start
agentdb_pattern_store (the first store auto-creates the schema), or via CLI:
npx agentdb@latest init ./memory.rvf
*.rvf to .gitignore unless the user explicitly wants memory checked into source control..rvf is a single binary file. Back it up like a SQLite database..rvf per coordinated namespace; use separate files for trust-boundary isolation.agentdb_pattern_*, agentdb_reflexion_*, etc.) or the npm library (import { SelfLearningRvfBackend } from 'agentdb')..rvf file by default — it can hold session-specific data, including content from messages.init on an existing .rvf file without confirming — it will refuse rather than overwrite, but a confused user might delete the existing file thinking it's stale.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.
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.
Search and manage hyperedges — n-ary relationships between memories. Use for swarm membership, multi-cause incidents, or any "this involves all of (A, B, C, D)" relationship that doesn't fit a binary edge.