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orchestrate-batch-refactor

Plan and execute large refactors with dependency-aware work packets and parallel analysis.

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  • SKILL.md3.8 KB
  • agents/openai.yaml271 B
  • references/agent-prompt-templates.md1.3 KB
  • references/work-packet-template.md997 B

SKILL.md(원문)

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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.
  • 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 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:

  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.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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