orchestrate-batch-refactor
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
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
explorersub-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
Subscribers only
The full skill, its 4 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | Dimillian/Skills (MIT) |
|---|---|
| License | — |
| Risk label | safe ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md, agents/openai.yaml, references/agent-prompt-templates.md, references/work-packet-template.md |
| Added | 2026-03-25 |
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