orchestrate-batch-refactor
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
- License 许可
- Apache-2.0
- 最近更新
- 2026-06-23
- Stars
- ★ 6
- Forks
- 0
01 前言 Front Matter
- name
- orchestrate-batch-refactor
- risk
- safe
- source
- Dimillian/Skills (MIT)
- date_added
- 2026-03-25
- description
- Plan and execute large refactors with dependency-aware work packets and parallel analysis.
02 资源文件 4
- 参考文档agents/openai.yaml271 B
- 参考文档references/agent-prompt-templates.md1.3 KB
- 参考文档references/docs-map.md9.8 KB
- 参考文档references/work-packet-template.md997 B
03 SKILL.md 正文
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 independent packets in parallel.
- Execute with worker agents.
- Spawn one
workerper independent packet. - Assign explicit ownership (files/responsibility).
- Instruct every worker that they are not alone in the codebase and must ignore unrelated edits.
- Spawn one
- 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.
Planning Contract
Every packet must include:
- Packet ID and objective.
- Owned files.
- Dependencies (none or packet IDs).
- Risks and invariants to preserve.
- Required checks.
- Integration notes for main thread.
Use references/work-packet-template.md for the exact shape.
Agent Prompting Contract
- Use the prompt templates in
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:
- Packet-level checks (fast and scoped).
- Cross-packet integration checks.
- Full project safety checks when scope is broad.
Prefer fast feedback loops, but never skip required behavior checks.
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