batch

分类: 工具与效率 | 上传者: remorses | 下载: 0 | 版本: v1.0（最新）

研究并计划大规模变更，然后在 5-30 个独立工作树代理（每个代理打开一个 PR）之间并行执行。当用户想要对许多文件进行彻底的机械更改（迁移、重构、批量重命名）时使用，这些更改可以分解为独立的并行单元。

更新日志: Source: GitHub https://github.com/remorses/kimaki

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📄 SKILL.md 5.1 KB

SKILL.md

---
name: batch
description: >
  Research and plan a large-scale change, then execute it in parallel across
  5-30 isolated worktree agents that each open a PR. Use when the user wants
  to make a sweeping, mechanical change across many files (migrations,
  refactors, bulk renames) that can be decomposed into independent parallel
  units.
argument-hint: "<instruction>"
source-path: cli.js (line 7359, function mGz)
source-package: "@anthropic-ai/claude-code@2.1.63"
source-date: 2026-02-28
---

# Batch: Parallel Work Orchestration

You are orchestrating a large, parallelizable change across this codebase.

## User Instruction

$instruction

## Phase 1: Research and Plan (Plan Mode)

Enter plan mode, then:

1. **Understand the scope.** Launch one or more Explore agents (in the foreground — you need their results) to deeply research what this instruction touches. Find all the files, patterns, and call sites that need to change. Understand the existing conventions so the migration is consistent.

2. **Decompose into independent units.** Break the work into 5–30 self-contained units. Each unit must:
   - Be independently implementable in an isolated git worktree (no shared state with sibling units)
   - Be mergeable on its own without depending on another unit's PR landing first
   - Be roughly uniform in size (split large units, merge trivial ones)

   Scale the count to the actual work: few files → closer to 5; hundreds of files → closer to 30. Prefer per-directory or per-module slicing over arbitrary file lists.

3. **Determine the e2e test recipe.** Figure out how a worker can verify its change actually works end-to-end — not just that unit tests pass. Look for:
   - A browser-automation tool (for UI changes: click through the affected flow, screenshot the result)
   - A tmux or CLI-verifier skill (for CLI changes: launch the app interactively, exercise the changed behavior)
   - A dev-server + curl pattern (for API changes: start the server, hit the affected endpoints)
   - An existing e2e/integration test suite the worker can run

   If you cannot find a concrete e2e path, ask the user how to verify this change end-to-end. Offer 2–3 specific options based on what you found (e.g., "Screenshot via chrome extension", "Run `bun run dev` and curl the endpoint", "No e2e — unit tests are sufficient"). Do not skip this — the workers cannot ask the user themselves.

   Write the recipe as a short, concrete set of steps that a worker can execute autonomously. Include any setup (start a dev server, build first) and the exact command/interaction to verify.

4. **Write the plan.** In your plan file, include:
   - A summary of what you found during research
   - A numbered list of work units — for each: a short title, the list of files/directories it covers, and a one-line description of the change
   - The e2e test recipe (or "skip e2e because …" if the user chose that)
   - The exact worker instructions you will give each agent (the shared template)

5. Present the plan for approval.

## Phase 2: Spawn Workers (After Plan Approval)

Once the plan is approved, spawn one background agent per work unit using the Task tool. **All agents must use `isolation: "worktree"` and `run_in_background: true`.** Launch them all in a single message block so they run in parallel.

For each agent, the prompt must be fully self-contained. Include:
- The overall goal (the user's instruction)
- This unit's specific task (title, file list, change description — copied verbatim from your plan)
- Any codebase conventions you discovered that the worker needs to follow
- The e2e test recipe from your plan (or "skip e2e because …")
- The worker instructions below, copied verbatim:

```
After you finish implementing the change:
1. **Simplify** — Invoke the simplify skill to review and clean up your changes.
2. **Run unit tests** — Run the project's test suite (check for package.json scripts, Makefile targets, or common commands like `npm test`, `bun test`, `pytest`, `go test`). If tests fail, fix them.
3. **Test end-to-end** — Follow the e2e test recipe from the coordinator's prompt (below). If the recipe says to skip e2e for this unit, skip it.
4. **Commit and push** — Commit all changes with a clear message, push the branch, and create a PR with `gh pr create`. Use a descriptive title. If `gh` is not available or the push fails, note it in your final message.
5. **Report** — End with a single line: `PR: <url>` so the coordinator can track it. If no PR was created, end with `PR: none — <reason>`.
```

Use `subagent_type: "general-purpose"` unless a more specific agent type fits.

## Phase 3: Track Progress

After launching all workers, render an initial status table:

| # | Unit | Status | PR |
|---|------|--------|----|
| 1 | <title> | running | — |
| 2 | <title> | running | — |

As background-agent completion notifications arrive, parse the `PR: <url>` line from each agent's result and re-render the table with updated status (`done` / `failed`) and PR links. Keep a brief failure note for any agent that did not produce a PR.

When all agents have reported, render the final table and a one-line summary (e.g., "22/24 units landed as PRs").

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