Agent skill · software engineering · sickn33
orchestrate-batch-refactor
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Why this skill is useful
Adds a comprehensive workflow for orchestrating complex refactors with dependency management and parallel execution that the AI cannot generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/orchestrate-batch-refactor--1bef94