Orchestrate Batch Refactor

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/orchestrate-batch-refactor

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Orchestrate Batch Refactor

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Inputs, Planning Contract, Agent Prompting Contract, Validation Strategy, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• When a refactor spans many files or subsystems and needs clear work partitioning.
• When you need dependency-aware planning before parallel implementation.
• 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.
• Use when the request clearly matches the imported source intent: Plan and execute large refactors with dependency-aware work packets and parallel analysis.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

references/agent-prompt-templates.md

references/work-packet-template.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Define scope and success criteria.
2. List target paths/modules and non-goals.
3. State behavior constraints (for example: preserve external behavior).
4. Run parallel analysis first.
5. Split target scope into analysis lanes.
6. Spawn explorer sub-agents in parallel to analyze each lane.
7. Ask each agent for: intent map, coupling risks, candidate work packets, required validations.

Imported Workflow Notes

Imported: 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.

Imported: 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.

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @orchestrate-batch-refactor to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @orchestrate-batch-refactor against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @orchestrate-batch-refactor for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @orchestrate-batch-refactor using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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.
• Do not start worker execution before plan synthesis is complete.
• Do not parallelize across unresolved dependencies.

Imported Operating Notes

Imported: 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.

Imported: 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.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/orchestrate-batch-refactor

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @10-andruia-skill-smith-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @20-andruia-niche-intelligence-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @2d-games

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/agent-prompt-templates.md

examples

examples/n/a

scripts

scripts/n/a

agents

agents/openai.yaml

assets

assets/n/a

• agent-prompt-templates.md
• work-packet-template.md
• openai.yaml
• openai.yaml
• agent-prompt-templates.md
• work-packet-template.md

Imported Reference Notes

Imported: 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

Imported: 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.

Imported: 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.

Imported: 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.