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Batch Orchestration

CommunityPopular
rohitg00
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.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namebatch-orchestration
Stars
2.9K
Forks
286
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Batch Orchestration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/batch-orchestration .claude/skills/batch-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Batch Orchestration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Batch Orchestration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Batch Orchestration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 CaseWhy Batch Works
API migrationsSame pattern across many endpoints
Dependency upgradesFind/replace + fix across codebase
Codemod-style refactorsMechanical transformation, file by file
Adding instrumentationSame tracing/logging pattern everywhere
Test coverage gapsAdd tests to untested modules independently
Lint rule adoptionApply new rule fixes across all files

Anti-Patterns

Don't BatchWhy
Interdependent changesUnits can't run in parallel if they depend on each other
Shared state modificationsMultiple agents writing to the same config or state file
Architecture changesNeed holistic reasoning, not file-by-file
Schema migrationsDatabase changes must be sequential
Changes requiring human judgment per fileDefeats the purpose of automation

Relationship to Other Patterns

PatternScaleIsolation
Direct edit1-3 filesNone needed
Subagent1 focused taskForked context
Worktree1 feature branchFull repo copy
Agent teams3-5 parallel tasksShared task list
Batch5-30 identical patternFull 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

Frequently asked questions

What does the Batch Orchestration AI 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.

Why use Batch Orchestration on TypingMind?

Because you install it once and use it with any model. Batch Orchestration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Batch Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/batch-orchestration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Batch Orchestration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Batch Orchestration?

As many as you like. As long as a model supports skills, you can use Batch Orchestration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Batch Orchestration AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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