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dispatching-parallel-agents

Splits a coding task into independent parallel agent workstreams with disjoint write sets, explicit inputs and outputs, dependency ordering, and a merge/review plan. Use when several subtasks can proceed without shared mutable state. Not for tightly coupled edits, one failing root cause, or parallel changes to the same files.

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Dispatching Parallel Agents

Parallelism reduces elapsed time only when it does not multiply integration ambiguity.

Dispatch checklist

  1. Define the shared outcome and acceptance evidence.
  2. Map dependencies and identify the critical path.
  3. Split by coherent responsibility, not arbitrary file count.
  4. Give every agent a disjoint write set and a narrow prompt.
  5. State repository rules, interfaces, expected artifacts, and stop conditions.
  6. Decide who integrates, resolves conflicts, and runs the final proof.

Good units are an independent reference/eval update, a script with its tests, or separate adapters with a stable manifest contract. Bad units are two agents editing the same public skill, guessing an interface that another agent owns, or investigating one symptom with competing hypotheses.

Handoff contract

Each agent returns:

  • files changed;
  • decisions and assumptions;
  • commands run and exact result;
  • known gaps or conflicts;
  • follow-up needed by the integrator.

Treat agent output as untrusted work product. Read the diff and rerun the relevant checks before merging. Do not claim the aggregate task is complete because every agent reported success.

Completion condition

Parallel work is integrated only after interface compatibility, conflict resolution, focused checks, and one fresh aggregate verification pass.

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概要と使いどころ

Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Closes a completed development branch by checking the final diff and proof, presenting merge, pull-request, keep, or discard options, and cleaning up only after the user or repository workflow chooses a path. Use when feature work is complete and the branch must be integrated or retired. Not for claiming a feature is complete before verification.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

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