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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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Start with the Factory guide 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.
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Use in the BuilderIO/skills repo whenever adding, updating, publishing, documenting, validating, or wiring a public skill. Covers the repo-local skill files, root catalog docs, plugin metadata, @agent-native/skills dynamic install path, optional managed AGENTS/CLAUDE instruction blocks in ../agent-native/framework, and generated/synced Plan skill gotchas.
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Use when asked to watch, babysit, audit, review, compare, or fix another agent's work from a Codex session ID, Claude Code session/transcript, chat/thread link, PR, branch, log, or pasted run summary. Monitor until the other agent is done or blocked, reconstruct what the user asked, independently investigate the same problem to form your own hypotheses and approach, inspect what the agent actually changed and verified, compare the two investigations, report gaps and add-on directions, and optionally make scoped fixes when the user authorizes repair.
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Open and operate Agent-Native workspace apps through Dispatch MCP, with inline app surfaces, browser-session authentication, and current screen state.
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Use when running Claude Fable on codebase-heavy or token-heavy work and the user wants Fable to orchestrate research, coding, and testing while cheaper subagents do bounded heavy lifting.
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Apply the same orchestration as `/efficient-fable` to any high-cost frontier model: delegate research, coding, and testing to cheaper subagents while keeping planning, synthesis, and final review with the expensive model.
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Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or host automations.
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