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orchestrate-batch-refactor

Plans and executes large code changes in an organized way. Breaks work into smaller parts and analyzes dependencies to do everything in parallel.

What it does

This tool helps organize and execute large code changes in a project. It breaks down the work into smaller parts and manages dependencies between them to ensure everything is done safely and in an organized manner.

When to use it

  • ✓When a change affects many parts of the code.
  • ✓When you need to plan the steps before making changes.
  • ✓When multiple people need to work on different parts simultaneously.

Example of what to ask your AI

“Organize a large refactor in the 'src/components' directory to improve performance, ensuring current functionality remains unchanged.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/orchestrate-batch-refactor/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/orchestrate-batch-refactor/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

First, the tool analyzes all parts of the code that need changing. Then, it creates a detailed plan, dividing the work into smaller tasks that can be done in parallel, respecting the dependencies between them.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill orchestrate-batch-refactor

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/orchestrate-batch-refactor/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Orchestrate Batch Refactor

Overview

Use this skill to run high-throughput refactors safely. Analyze scope in parallel, synthesize a single plan, then execute independent work packets with sub-agents.

When to Use

  • When a refactor spans many files or subsystems and needs clear work partitioning.
  • When you need dependency-aware planning before parallel implementation.

Inputs

  • Repo path and target scope (paths, modules, or feature area)
  • Goal type: refactor, rewrite, or hybrid
  • Constraints: behavior parity, API stability, deadlines, test requirements

When to Use Parallelization

  • Use this skill for medium/large scope touching many files or subsystems.
  • Skip multi-agent execution for tiny edits or highly coupled single-file work.

Core Workflow

  1. Define scope and success criteria.
    • List target paths/modules and non-goals.
    • State behavior constraints (for example: preserve external behavior).
  2. Run parallel analysis first.
    • Split target scope into analysis lanes.
    • Spawn explorer sub-agents in parallel to analyze each lane.
    • Ask each agent for: intent map, coupling risks, candidate work packets, required validations.
  3. Build one dependency-aware plan.
    • Merge explorer output into a single work graph.
    • Create work packets with clear file ownership and validation commands.
    • Sequence packets by dependency level; run only independent packets in parallel.
  4. Execute with worker agents.
    • Spawn one worker per independent packet.
    • Assign explicit ownership (files/responsibility).
    • Instruct every worker that they are not alone in the codebase and must ignore unrelated edits.
  5. Integrate and verify.
    • Review packet outputs, resolve overlaps, and run validation gates.
    • Run targeted tests per packet, then broader suite for integrated scope.
  6. Report and close.
    • Summarize packet outcomes, key refactors, conflicts resolved, and residual risks.

Work Packet Rules

  • One owner per file per execution wave.
  • No parallel edits on overlapping file sets.
  • Keep packet goals narrow and measurable.
  • Include explicit done criteria and required checks.
  • Prefer behavior-preserving refactors unless user explicitly requests behavior change.

Planning Contract

Every packet must include:

  1. Packet ID and objective.
  2. Owned files.
  3. Dependencies (none or packet IDs).
  4. Risks and invariants to preserve.
  5. Required checks.
  6. Integration notes for main thread.

Use references/work-packet-template.md for the exact shape.

Agent Prompting Contract

  • Use the prompt templates in references/agent-prompt-templates.md.
  • Explorer prompts focus on analysis and decomposition.
  • Worker prompts focus on implementation and validation with strict ownership boundaries.

Safety Guardrails

  • Do not start worker execution before plan synthesis is complete.
  • Do not parallelize across unresolved dependencies.
  • Do not claim completion if any required packet check fails.
  • Stop and re-plan when packet boundaries cause repeated merge conflicts.

Validation Strategy

Run in this order:

  1. Packet-level checks (fast and scoped).
  2. Cross-packet integration checks.
  3. Full project safety checks when scope is broad.

Prefer fast feedback loops, but never skip required behavior checks.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ

What does 'refactor' mean?

Refactoring means improving the internal structure of the code without changing its external behavior. It's like tidying up the wiring in a house to make it easier to understand and maintain, without altering how the appliances work.

What are 'work packets'?

These are small tasks into which the larger job is divided. Each packet has a clear objective, specific files to work on, and rules to ensure there are no conflicts with other tasks.

Why analyze in parallel?

Analyzing in parallel means different parts of the code are examined at the same time by different 'agents' (small AI assistants). This speeds up understanding the scope and identifying risks before starting the changes.

Details

Original description by the author

Plan and execute large refactors with dependency-aware work packets and parallel analysis.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#3775
License
MIT
Updated
10/15/2026

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