batch-orchestration
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Best use case
batch-orchestration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Teams using batch-orchestration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/batch-orchestration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How batch-orchestration Compares
| Feature / Agent | batch-orchestration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Batch Orchestration The `/batch` command pattern for large-scale parallel changes. ## How It Works ```text /batch <instruction> │ ├── 1. Research: scan repo, understand scope ├── 2. Decompose: split into 5-30 independent units ├── 3. Present plan: show units, ask for approval ├── 4. Execute: one background agent per unit in isolated worktree └── 5. Collect: each agent runs tests and opens a PR ``` ## Syntax ```bash /batch Convert all React class components to function components /batch Add error boundaries to every page component /batch Migrate from moment.js to dayjs across the codebase /batch Add OpenTelemetry tracing to all API handlers ``` The instruction should describe the change pattern, not individual files. The batch system finds the files. ## Phase 1: Research The orchestrator scans the repo to find every instance that matches the instruction: ```bash grep -r "class.*extends.*Component" --include="*.tsx" -l ``` It builds a complete list of targets and groups them by independence. ## Phase 2: Decompose Each unit must be: - **Independent** — no shared state with other units - **Self-contained** — can be implemented and tested alone - **Verifiable** — has a clear pass/fail criteria **Good units:** ```text Unit 1: Convert src/components/Header.tsx (class → function) Unit 2: Convert src/components/Footer.tsx (class → function) Unit 3: Convert src/components/Sidebar.tsx (class → function) ``` **Bad units:** ```text Unit 1: Convert all components in src/components/ (too broad) Unit 2: Fix issues from Unit 1 (dependent) ``` Target 5-30 units. Fewer than 5 doesn't justify the overhead. More than 30 and coordination costs grow. ## Phase 3: Plan Approval The orchestrator presents: ```text BATCH: Convert class components to function components Found: 18 class components across src/ Units (18): 1. src/components/Header.tsx — class Header → function 2. src/components/Footer.tsx — class Footer → function ... 18. src/pages/Settings.tsx — class Settings → function Per unit: convert class to function, update hooks, run component tests Estimated: ~2 min per unit, ~5 min total (parallel) Proceed? (y/n) ``` **Wait for approval.** Never spawn agents without explicit confirmation. ## Phase 4: Execute After approval, for each unit: 1. Create isolated git worktree 2. Spawn background agent in that worktree 3. Agent implements the change 4. Agent runs relevant tests 5. Agent opens a PR Agents run in parallel. Each has its own context window and worktree — no conflicts. ```text [Agent 1] ── worktree-1 ── Header.tsx ── tests pass ── PR #41 [Agent 2] ── worktree-2 ── Footer.tsx ── tests pass ── PR #42 [Agent 3] ── worktree-3 ── Sidebar.tsx ── tests fail ── flagged ``` ## Phase 5: Collect After all agents complete: - Summary of pass/fail per unit - Links to opened PRs - Any units that failed with error details - Failed units can be retried individually ## Best For | Use Case | Why Batch Works | |----------|-----------------| | API migrations | Same pattern across many endpoints | | Dependency upgrades | Find/replace + fix across codebase | | Codemod-style refactors | Mechanical transformation, file by file | | Adding instrumentation | Same tracing/logging pattern everywhere | | Test coverage gaps | Add tests to untested modules independently | | Lint rule adoption | Apply new rule fixes across all files | ## Anti-Patterns | Don't Batch | Why | |-------------|-----| | Interdependent changes | Units can't run in parallel if they depend on each other | | Shared state modifications | Multiple agents writing to the same config or state file | | Architecture changes | Need holistic reasoning, not file-by-file | | Schema migrations | Database changes must be sequential | | Changes requiring human judgment per file | Defeats the purpose of automation | ## Relationship to Other Patterns | Pattern | Scale | Isolation | |---------|-------|-----------| | Direct edit | 1-3 files | None needed | | Subagent | 1 focused task | Forked context | | Worktree | 1 feature branch | Full repo copy | | Agent teams | 3-5 parallel tasks | Shared task list | | **Batch** | 5-30 identical pattern | Full worktree per unit | Batch is the heaviest tool. Use it when the change is mechanical, repetitive, and the units are truly independent. ## Guardrails - Always review the decomposition before approving - Each agent must run tests before opening a PR - Failed units get flagged, not silently skipped - Clean up worktrees after all agents complete - Review PRs in batches — don't merge blindly
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