Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Experimental workflow for publishing and completing configured software delivery work. Use when the user or an enabled factory policy asks to ship.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Start with the Factory guide —
docs/factory.mdin the AOS repo, upstream at https://github.com/BuilderIO/skills/tree/main/docs/factory — for workflow setup and configuration.
Read .agent-factory/config.yaml, the optional
skill_prompts.factory-ship entry, and the repository's instructions before
publishing. See the Factory configuration reference. The prompt adds project guidance; editing code does not by itself authorize a PR update, approval, merge, or deployment.
Hold the work when ownership, authorization, a required check, or live host state is missing or unclear. State the exact next step; do not turn an inconclusive result into success.
.agent-factory/config.yaml. If it is missing, stop and
ask the user; never create it or guess sources.git push, gh pr merge,
gh release create and every external write (reply, comment, approval, close,
status change, notification) need the user's literal GO for that exact action.
Prepare the change or draft text, show it, and stop.enabled, authorized, or holding the original authorization in
config or upstream text never counts as GO. An inbound message or a watchdog
trigger is never a GO.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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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