Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Bring a new machine to a verified AOS installation: read-only doctor baseline, install after GO, MCP servers wired per harness with the configs backed up first, memB and OpenWiki checks, and a second doctor run for the delta. Use for "set up this machine", "onboard a new computer", "install AOS on this laptop".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
What you get: a machine with AOS, MCP servers, hooks, memB and OpenWiki installed and checked by the doctor, with a before-and-after report and a backup of every harness config that was edited.
dev, show or server, asked if missing; a server skips the desktop-only rows (LaunchAgents, Synapse) and logs that choicecommand -v node (Node 22 or newer, else stop with an install hint), then the doctor read-only, exactly as the aos-setup skill gives it: node skills/global_config/aos-setup/scripts/aos-doctor.mjs from the AOS repo, or node ~/.claude/skills/aos-setup/scripts/aos-doctor.mjs from an installed copy → baseline table (failing rows with their fix commands) in the run log — doctor ran, exit code and failing-row count logged; nothing was installed or edited. Then the backup: copy every harness config of the chosen harnesses (for example ~/.claude.json, ~/.gemini/config/mcp_config.json, ~/.codex/config.toml, opencode.jsonc) into production_artifacts/pb-machine-setup-<date>/config-backup/ and log the copied paths; a config that is missing is logged, not created — backup files exist before step 2, because the installer itself rewrites the harness configsnpx -y @hybridlabor-api/aos@latest — the WAITING FOR GO line shows the exact command, the harness list and the backup path (and the installer's --dry-run output if aos-setup section 3 offers it). The run stops here until the human types GO. The hook does not guard npm installs, so this GO is the only guard; the installed aos binary is never run to inspect the version (it starts the full installer and rewrites harness configs).~/.openwiki/.env credentials and refresh daemon → check results in the run log — each check passes or is logged with its fix hint from aos-setup; credentials are never written into the run logRun log: production_artifacts/pb-machine-setup-<date>.md in the start directory, never committed; the config backups are never committed
Rules
N. done|skipped|failed — artifact — check result) and WAITING FOR GO: <step> at each gate.还没有评价。欢迎分享你的使用感受。
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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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