Plan Arbiter
Builder.ioOfficialPublished by Builder.io
- Skill
- Meta (authoring, orchestration, planning, memory)
- Agent tooling
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Plan Arbiter provides a systematic process to evaluate multiple proposed strategies from different AI agents. It normalizes competing plans, cross-reviews them against project requirements and existing codebase context, and produces a final, actionable execution memo. The skill focuses on identifying the most grounded and effective approach, whether by adopting one plan, creating a hybrid, or requesting revisions.
What it does
- Normalizes disparate plans into comparable claims and objectives.
- Cross-reviews plans against project context, code, and requirements.
- Identifies hidden dependencies, risks, and complementary strengths.
- Produces a final decision memo with an execution sequence and verification steps.
- Recommends the most suitable agent or model for the final implementation.
- Maintains a clear distinction between planning and execution phases.
When to use it
- When you have multiple conflicting plans from different AI agents.
- When you need to merge the best components of several proposed strategies.
- When you require an objective review of a plan's feasibility against your codebase.
- When you need to decide which agent is best suited to execute a specific task.
Advantages
- Reduces ambiguity by forcing a clear, unified execution path.
- Ensures plans are grounded in real files, APIs, and project constraints.
- Provides transparency by documenting rejected alternatives and reasoning.
- Includes explicit verification gates to ensure the chosen plan is sound.
Usage examples
- “I have two different implementation plans from Claude and Codex for this feature. Can you arbitrate?”
- The agent will collect both plans, normalize their steps, compare them against your codebase, and provide a final decision memo with a recommended execution path.
- “Review these three PR descriptions and tell me which one is the most viable for our current architecture.”
- The agent will cross-review the PR descriptions, identify risks or missing dependencies, and recommend the best approach or a hybrid version.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/builderio/plan-arbiter/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip plan-arbiter.zip -d .agents/skills
Documentation by Builder.io
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
Collect the source plans.
Normalize each plan into comparable claims.
Cross-review the plans against each other and the real codebase or task context.
Choose a winner, merge a better hybrid, or send the plans back for revision.
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:
Correctness and fit to the user's request.
Grounding in real files, APIs, tests, data, and UI behavior.
Simpler first implementation that does not block the intended future.
Better validation and rollback story.
Lower token/time cost for execution once quality is acceptable.
Handoff
Return a compact decision memo:
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.
Files
- ATTRIBUTION.mdAdded by AutoGineer849 B
- LICENSELicence file added by AutoGineer1.0 KiB
- README.md1.3 KiB
- SKILL.md4.0 KiB
- agents/openai.yaml224 B
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Builder.io
- Publisher
- Builder.io (Official)
- Source
- BuilderIO/skills · skills/plan-arbiter · commit f4f10b8
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Builder.io.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download plan-arbiter.zip