Orchestrate Batch Refactor

作者 dimillian05ba982bfeb0無授權條款3.9K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫6 個月前更新

Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware execution.

AI 產生的概覽

透過平行子代理分析與相依性感知的工作包,規劃並協調大規模多檔案重構。

功能
這項技能提供一套工作流程,用於在大量檔案中執行重構、重寫或混合改造。它會把目標範圍拆成多個分析通道,平行啟動 explorer 子代理,將其結果合併成一張相依性感知的工作圖,再以具有明確檔案歸屬的 worker 代理執行彼此獨立的工作包。它會產出計畫、附驗證指令的工作包,以及涵蓋成果、衝突與殘餘風險的收尾報告。
適用情境
當重構或重寫涉及大量檔案或子系統、需要協調平行工作時使用。它適合可拆分工作流並依相依性排序的中大型範圍任務。小型修改或高度耦合的單一檔案工作應跳過這種多代理做法。
執行需求
不隨附指令碼,僅為指示。它依賴代理啟動 explorer 與 worker 子代理的能力,以及對目標程式碼儲存庫的存取。它引用了兩份隨附的參考文件,用於工作包與提示詞範本。

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.

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 [blocked] for the exact shape.

Agent Prompting Contract

  • Use the prompt templates in references/agent-prompt-templates.md [blocked].
  • 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.

來源與署名

來源:dimillian/skills位於orchestrate-batch-refactor提交05ba982

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