Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
When this skill is invoked via /memb-ingest or requested, you MUST orchestrate a deep scan of the user-specified project path or conversation logs to populate the memB local vector database (~/.MemBDB/memb.db).
--transcripts) or filter specific file patterns (e.g., *.md, agent.md, .openwiki/).Locate the memb-mcp installation (e.g., ~/.gemini/config/mcps/memb-mcp/ or ~/.gemini/mcps/memb-mcp/) and execute the ingestion script via its virtual environment:
# Run ingestion with project detection & optional transcript indexing
~/.gemini/config/mcps/memb-mcp/.venv/bin/python ~/.gemini/config/mcps/memb-mcp/memb_ingest.py "<TARGET_DIRECTORY>" --transcripts
Query memB using search_memory or inspect ~/.MemBDB/memb.db to confirm that the project architecture and key decisions are indexed.
node_modules, .venv, .git, dist, and temporary cache directories.This skill provides domain-specific logic and rules for its respective BDB pipeline component to ensure standardization across multi-agent workflows.
| Rationalization | Reality |
|---|---|
| "The code change was small, so I skipped updating OpenWiki docs." | Every state change must be reflected in the relevant system records. |
| "The ingest script exited without an error, so the memB index must be updated." | Silent failures happen; explicit verification of the side effect is mandatory. |
| "I'll let the /startcycle proceed without a defined rollback path." | Proceeding without a rollback path corrupts the workflow integrity and safety. |
| "I trust the cached agent registry instead of rescanning after a skill change." | Caches stale out quickly; explicit rescans prevent ghost failures. |
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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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