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. It is especially useful for teams working in multi. Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "orchestrate-batch-refactor" skill to help with this workflow task. Context: Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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.
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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.
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