Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Installation
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Plan and execute large refactors with dependency-aware work packets and parallel analysis.
risk
safe
source
Dimillian/Skills (MIT)
date_added
2026-03-25
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
Define scope and success criteria.
List target paths/modules and non-goals.
State behavior constraints (for example: preserve external behavior).
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.
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.
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.
Integrate and verify.
Review packet outputs, resolve overlaps, and run validation gates.
Run targeted tests per packet, then broader suite for integrated scope.
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.