Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Model Context Protocol interface to the memB persistent agent memory layer. Use when an agent needs to add, search, list or delete persistent memories locally and offline. Exposes add_memory, search_memory, list_memories and delete_memory.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
The memb-mcp server provides a standard Model Context Protocol (MCP) interface to the memB persistent agent memory layer. It allows any compatible developer agent (such as Cursor, Google Antigravity, Claude Code, or VS Code Cline) to read, write, and query persistent memories locally and offline.
memb-mcp__MCPS_DIR__/memb-mcp/.venv/bin/python (macOS/Linux) or __MCPS_DIR__/memb-mcp/.venv/Scripts/python.exe (Windows)["__MCPS_DIR__/memb-mcp/run.py"]MEMB_DATA_DIR: Path to the SQLite database and config folder (defaults to ~/.MemBDB).GEMINI_API_KEY: User Google API key for reasoning.add_memorySaves a new fact, coding preference, or workaroud to the local SQLite database.
text (string, required): The fact or guideline to store.user_id (string, optional): Owner of the row. Default: env MEMB_USER_ID (fallback $USER). Pass a group id from MEMB_GROUP_IDS only to write a shared group row on purpose.category (string, optional): Default project_card when project_id is set, else task_learnings. Options: project_card, architecture_decisions, bug_fixes, coding_conventions, tooling_setup, anti_patterns, task_learnings, user_preferences, dependency_decisions, performance_findings, security_constraints, testing_patterns, data_model, api_contracts, deployment_runbook, team_norms, domain_glossary, godmode.project_id (string, optional): Active workspace folder to isolate search queries.source, harness, session (string, optional): Provenance, stored in the row metadata.search_memoryQueries both global godmode memory and the active project_id memories in parallel, returning semantic matches ranked by cosine similarity.
query (string, required): Keyword or semantic question.user_id (string, optional): Restrict to one owner id. Default: the user plus the group ids in MEMB_GROUP_IDS (default bdb_developer).limit (integer, optional, default: 5): Maximum matching memories to return.project_id (string, optional): Active workspace folder.list_memoriesLists all memories currently registered in the database for the active user.
user_id (string, optional): Restrict to one owner id. Default: the user plus MEMB_GROUP_IDS.limit (integer, optional, default: 50): Maximum results.delete_memoryDeletes a specific memory segment using its UUID.
memory_id (string, required): The unique UUID of the memory item.The memb-mcp server has no automatic credential filter today: it stores whatever an agent sends it.
memb-skill) plus human review of stored memories.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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