Development
orchestrate-batch-refactor - Claude MCP Skill
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
SEO Guide: Enhance your AI agent with the orchestrate-batch-refactor tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to plan and execute large refactors with dependency-aware work packets and parallel analysis.... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# 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.
Signals
Information
- Repository
- arlenagreer/claude_configuration_docs
- Author
- arlenagreer
- Last Sync
- 5/10/2026
- Repo Updated
- 5/7/2026
- Created
- 4/10/2026
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