Plan Arbiter logo

Plan Arbiter

OrganizationPopular
BuilderIO
plan-arbiter

Use when asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's proposals.

Overview

PublisherBuilderIO
Repositoryskills
Skill nameplan-arbiter
Stars
4.3K
Forks
211
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BuilderIO on GitHub. Read the source before you install it.

Installation

Install the Plan Arbiter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BuilderIO/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/plan-arbiter .claude/skills/plan-arbiter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Arbiter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Plan Arbiter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Plan Arbiter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Plan Arbiter

Turn competing plans into one executable direction. Preserve the best ideas, reject weak assumptions, and produce a clear handoff instead of a blended mush.

Workflow

  1. Collect the source plans.
  2. Normalize each plan into comparable claims.
  3. Cross-review the plans against each other and the real codebase or task context.
  4. Choose a winner, merge a better hybrid, or send the plans back for revision.
  5. Produce one execution handoff with verification gates and rejected alternatives.

Planning is read-only unless the user explicitly asks you to implement after the decision.

Collect Source Plans

Accept plans as pasted text, local files, session IDs, transcript paths, PRs, comments, visual-plan links, or chat history. Resolve the original artifacts when possible so you can see prompt changes and assumptions that may be missing from a final summary.

If a plan is still being written and the user asked you to wait, monitor it until it is done or blocked. If a plan cannot be resolved, continue with the available plan text and mark the missing source as a risk.

Normalize

For each plan, extract:

  • Objective and scope.
  • Key assumptions and unresolved questions.
  • Proposed files, modules, APIs, data shapes, UI states, or workflows.
  • Implementation sequence.
  • Validation strategy.
  • Rollback or migration concerns.
  • Cost, complexity, and expected executor fit.

Do not reward verbosity. Prefer plans that are concrete, grounded in real code, and honest about tradeoffs.

Cross-Review

Review each plan as if another capable agent wrote it:

  • Check whether it satisfies the user's actual request.
  • Verify claims against the repo, docs, tests, screenshots, or external systems when those are relevant and available.
  • Identify hidden dependencies, missing tests, risky sequencing, vague steps, unnecessary scope, and hard-to-reverse decisions.
  • Notice complementary strengths: one plan may have the better architecture while another has the better migration or validation path.
  • Separate plan quality from executor preference. A cheaper/faster executor can be the right choice for implementation even when another model produced the best critique.

Use subagents for independent review when the plans are large, the codebase is wide, or the decision would benefit from separate technical and product passes.

Decide

Choose one of three outcomes:

  • Adopt: pick one plan mostly as written.
  • Hybrid: combine specific pieces into a stronger execution plan.
  • Revise first: request another planning pass because both plans miss a key constraint or depend on an unresolved decision.

Use this tie-break order:

  1. Correctness and fit to the user's request.
  2. Grounding in real files, APIs, tests, data, and UI behavior.
  3. Simpler first implementation that does not block the intended future.
  4. Better validation and rollback story.
  5. Lower token/time cost for execution once quality is acceptable.

Handoff

Return a compact decision memo:

md
Decision
- Adopt Plan A / Hybrid / Revise first.

Why
- The deciding evidence and tradeoffs.

Execution Plan
- Ordered steps with files or surfaces to touch.

Borrowed From Other Plans
- Useful pieces kept from non-winning plans.

Rejected
- Ideas intentionally not taking, with reasons.

Verification
- Tests, browser checks, screenshots, CI, review, or deploy checks needed.

Executor Recommendation
- Which agent/model should implement and why.

When the user already asked for execution and the chosen path is clear, proceed with the selected plan after reporting the decision briefly. Otherwise stop at the handoff and ask for approval.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Plan Arbiter AI skill do?

Use when asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's proposals.

Why use Plan Arbiter on TypingMind?

Because you install it once and use it with any model. Plan Arbiter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Plan Arbiter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BuilderIO/skills/tree/main/skills/plan-arbiter. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Plan Arbiter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Plan Arbiter?

As many as you like. As long as a model supports skills, you can use Plan Arbiter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Plan Arbiter AI skill free?

Yes. It is published on GitHub by BuilderIO under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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