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…
Skill by tensology
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/tensology-plan-arbiter
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/plan-arbiter
What's inside
- Skill name
plan-arbiter- Description
- 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.
- License
- MIT
- Instructions
- 111 lines in SKILL.md · view on GitHub
Files (2)
- README.md
- SKILL.md
SKILL.md (first lines)
# 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.
From tensology/decisionsai, MIT.
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Signals
- Stars
- 17
- Forks
- 2
- Open issues
- 0
- Last commit (this folder)
- Oct 2, 2026 (26 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/tensology/decisionsai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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architecture-deepening-review
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Use when reviewing a codebase for architectural friction, shallow modules, poor test seams, scattered behavior, duplicated orchestration, or opportunities to…
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Cite this page
plan-arbiter skill. Agent Mart. https://agentmart.dev/skills/tensology-plan-arbiter (last updated 2026-10-24)