orchestrate-batch-refactor
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Best use case
orchestrate-batch-refactor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Teams using orchestrate-batch-refactor 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/orchestrate-batch-refactor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orchestrate-batch-refactor Compares
| Feature / Agent | orchestrate-batch-refactor | 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?
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
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.
SKILL.md Source
# 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`](references/work-packet-template.md) for the exact shape. ## Agent Prompting Contract - Use the prompt templates in [`references/agent-prompt-templates.md`](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.
Related Skills
tdd-workflows-tdd-refactor
Use when working with tdd workflows tdd refactor
swiftui-view-refactor
Refactor SwiftUI views into smaller components with stable, explicit data flow.
fp-refactor
Comprehensive guide for refactoring imperative TypeScript code to fp-ts functional patterns
codebase-cleanup-refactor-clean
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
code-refactoring-tech-debt
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
code-refactoring-refactor-clean
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
code-refactoring-context-restore
Use when working with code refactoring context restore
azure-compute-batch-java
Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.
zustand-store-ts
Create Zustand stores following established patterns with proper TypeScript types and middleware.
zoom-automation
Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.
zoho-crm-automation
Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.
zod-validation-expert
Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.