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Agent skill

Orchestrate Batch Refactor

by sickn33 in sickn33/agentic-awesome-skills

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

MITAuto-check passedDevelopment

Install Orchestrate Batch Refactor

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill orchestrate-batch-refactor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills orchestrate-batch-refactor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/orchestrate-batch-refactor .claude/skills/orchestrate-batch-refactor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
orchestrate-batch-refactor
GitHub stars
47k
Used in
2 other repos
Token cost
~980 tokens
SKILL.md length
484 words
Files
4 (incl. references)
Skills in repo
1,394
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 6 steps: Define scope and success criteria. → Run parallel analysis first. → Build one dependency-aware plan. → …
  • Tasks that involve Refactoring
  • SKILL.md covers Overview, When to Use, Inputs and When to Use Parallelization, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Orchestrate Batch Refactor is an agent skill from sickn33/agentic-awesome-skills. Plan and execute large refactors with dependency-aware work packets and parallel analysis.

Its SKILL.md is about 980 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/agent-prompt-templates.md` and `references/work-packet-template.md`).

It sits in Development, covering Refactoring. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Refactoring

Example prompts

  • “/orchestrate-batch-refactor”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Define scope and success criteria.
  2. Run parallel analysis first.
  3. Build one dependency-aware plan.
  4. Execute with worker agents.
  5. Integrate and verify.
  6. Report and close.

What it can do on your machine

Read from SKILL.md and the folder at commit 1e53ce2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Orchestrate Batch Refactor loads about 980 tokens when it runs, and up to ~1.6k if it reads all its reference files. Until then it costs about 29 tokens; SKILL.md has 484 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~980
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1e53ce2, republished under its MIT licence (© sickn33). 484 words, ~980 tokens.

Download SKILL.mdSave it as .claude/skills/orchestrate-batch-refactor/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
orchestrate-batch-refactor
description
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
risk
safe
source
Dimillian/Skills (MIT)
date_added
2026-03-25

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.
Show full SKILL.md (186 more words)Show less

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.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/orchestrate-batch-refactor of sickn33/agentic-awesome-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/agent-prompt-templates.md
  • references/work-packet-template.md

Open the folder on GitHubat commit 1e53ce2

Used in 2 other repositories

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Orchestrate Batch Refactor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Orchestrate Batch Refactor compared with similar skills
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Orchestrate Batch Refactor this skillsickn33/agentic-awesome-skills47k2 repos~980Automated safety check: PassMIT
Guidelinesakash-network/node1.1k22 repos~577Automated safety check: PassMIT
Component Refactoringlangflow-ai/langflow156k—~3.5kAutomated safety check: PassMIT
Migrate Core Code to Submodulestinyhumansai/openhuman41k—~2.6kAutomated safety check: PassGPL-3.0
ast-grep Structural Searchcode-yeongyu/oh-my-openagent70k—~3.3kAutomated safety check: PassMIT
Systematic Code Refactoringluongnv89/claude-howto42k—~3kAutomated safety check: PassMIT

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Categories

Questions about Orchestrate Batch Refactor

What does Orchestrate Batch Refactor do?

Plan and execute large refactors with dependency-aware work packets and parallel analysis. Orchestrate Batch Refactor is an agent skill from sickn33/agentic-awesome-skills. Plan and execute large refactors with dependency-aware work packets and parallel analysis.

When should I use Orchestrate Batch Refactor?

Orchestrate Batch Refactor fits situations like: tasks that involve Refactoring.

How do I install Orchestrate Batch Refactor in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill orchestrate-batch-refactor -a claude-code`. Or copy the skill folder (skills/orchestrate-batch-refactor in sickn33/agentic-awesome-skills) into .claude/skills/orchestrate-batch-refactor in your project. Claude Code loads it when a task matches its description.

How do I install Orchestrate Batch Refactor in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill orchestrate-batch-refactor -a codex`. Or copy the skill folder (skills/orchestrate-batch-refactor in sickn33/agentic-awesome-skills) into .agents/skills/orchestrate-batch-refactor in your project. Codex loads it when a task matches its description.

Can I use Orchestrate Batch Refactor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill orchestrate-batch-refactor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/orchestrate-batch-refactor, .gemini/skills/orchestrate-batch-refactor, .github/skills/orchestrate-batch-refactor and .opencode/skills/orchestrate-batch-refactor in your project.

What does Orchestrate Batch Refactor need to run?

SKILL.md names no scripts, command-line tools or credentials: Orchestrate Batch Refactor is instructions for the agent only.

Does Orchestrate Batch Refactor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Orchestrate Batch Refactor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Orchestrate Batch Refactor use?

Orchestrate Batch Refactor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Orchestrate Batch Refactor use?

About 980 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 590 tokens, read only when the agent opens those files.

What are the alternatives to Orchestrate Batch Refactor?

Skills that share tags, products or a category with Orchestrate Batch Refactor: Guidelines (akash-network/node, 1.1k stars), Component Refactoring (langflow-ai/langflow, 156k stars), Migrate Core Code to Submodules (tinyhumansai/openhuman, 41k stars) and ast-grep Structural Search (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Orchestrate Batch Refactor?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,304 GitHub stars. The repository holds 1,394 skills in this directory. The repository was last updated on October 6, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.