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win-evaluate

Score content variants with WIN committee experts.

Skill by andrew-tomago

IndexedMIT
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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/andrew-tomago-win-evaluate

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About

Score content variants with WIN committee experts. Use to generate committee_matrix.json, compare drafts, and tune rubric weights or expert focus.

Use cases: writing

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Runs as
Shell scripts

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/win-evaluate

What's inside

Skill name
win-evaluate
Description
Score content variants with WIN committee experts. Use to generate committee_matrix.json, compare drafts, and tune rubric weights or expert focus.
License
MIT
Instructions
32 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (3)

  • SKILL.md
  • openai.yaml · agents/
  • run_evaluate.sh · scripts/

SKILL.md (first lines)

# WIN Evaluate

## Overview

Run only the committee scoring stage to produce `data/output/committee_matrix.json`.

## Workflow

1. Ensure `profiles.json` already exists (run profile stage first if needed).
2. Validate content and committee input contracts.
3. If required content/committee inputs are missing, please interview the user to acquire the necessary inputs before running commands.
4. Run committee evaluation and inspect weighted scores plus consensus spread.

## Command

```bash
scripts/run_evaluate.sh
```

## Input Contracts

Expected schemas are documented in:
- `../win-committee/references/data-contracts.md`

## Guardrails

- Keep scoring criteria explicit through `rubric_weights`.
- Avoid overfitting to one persona by checking expert weights and consensus spread.
- If required inputs are missing, please interview the user to acquire the necessary inputs.
- Keep user-specific drafts in `*.local.json` files.

From andrew-tomago/weighted-intelligence-nodes, MIT.

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Last commit (this folder)
Feb 6, 2026 (8 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
Repository
github.com/andrew-tomago/weighted-intelligence-nodes
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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Cite this page

win-evaluate skill. Agent Mart. https://agentmart.dev/skills/andrew-tomago-win-evaluate (last updated 2026-10-24)