hive.worker-delegation

1## Operational Protocol: Worker Delegation
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3**Applies when** you're in COLONY mode and considering whether (and how) to fan out work to parallel workers via `run_playbook`. Read this before fan-out, not during.
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5### Mental model: the tracker is the spine, the playbook is the controller
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7You don't coordinate workers by reading their reports and deciding what's next each turn. You model the goal as a **tracker table** where every unit of work is a row, and you write a **playbook** — a deterministic Python script — that drives that table to completion:
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9> The playbook queries the rows that aren't done yet, dispatches one worker per undone row, and re-queries until none are left. Workers advance their own rows. Re-running the playbook resumes — done rows simply aren't in the work-list anymore.
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11This is a reconciliation loop. The tracker is the state; the playbook is the controller that converges it. Three artifacts, three jobs:
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13- **Tracker table** — the durable work-list and its state. The row's status column *is* the progress.
14- **Skill** (`write_skill`) — the worker's operating procedure: schema, tool sequence, output format, quality bar. The risky part.
15- **Playbook** (`run_playbook`) — the deterministic orchestration: which rows are undone, who runs them, rate limits, retry/convergence policy. The cheap part.
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17The worker's task string carries only the per-row slice; everything reusable lives in the skill, everything deterministic lives in the playbook.
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