Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Turn a meeting transcript into clean notes, the decisions, and who does what by when, then draft the follow-up mail and send it only after your GO. Use for "meeting notes", "action items from this meeting", "who did what in this transcript", "who does what after the call".
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
What you get: clean notes, the decisions, and who does what by when, from a meeting transcript.
./pb-meeting-actions-<date>/run-log.md (create the folder only after the file check passes) — the file is readable; if you only have audio, ask for the transcript export instead (no transcription is available here)notes.md with attendees and at most 10 summary bullets — each bullet cites a timestamp or linedecisions.md — each decision traced to a transcript lineactions.md as a table: owner, task, due, source — an unknown owner or due date is written as ?, never guessedfollowup-draft.md, one mail per recipient group, each recipient as name + address (? blocks sending until you fill it) — draft only, nothing is sentfollowup-draft.md.Run log: run-log.md in the save folder. One line per step as it completes (N. done|skipped|failed — file — check result), and WAITING FOR GO: <step> at the gate. Anything other than the literal GO (case-insensitive) is not a GO. If a check fails, stop, write the failure into the log and tell the user.
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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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