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win-summary

Summarize WIN committee_matrix.json into ranked markdown feedback.

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-summary

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About

Summarize WIN committee_matrix.json into ranked markdown feedback. Use to surface consensus, disagreements, and top rewrite actions.

Use cases: documents

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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-summary

What's inside

Skill name
win-summary
Description
Summarize WIN committee_matrix.json into ranked markdown feedback. Use to surface consensus, disagreements, and top rewrite actions.
License
MIT
Instructions
32 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (3)

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

SKILL.md (first lines)

# WIN Summary

## Overview

Run only the synthesis stage to produce `data/output/summary.md`.

## Workflow

1. Ensure `committee_matrix.json` exists (run evaluation stage first if needed).
2. If required matrix input is missing, please interview the user to acquire the necessary inputs before running commands.
3. Generate markdown summary.
4. Use low-score rows as the first rewrite backlog.

## Command

```bash
scripts/run_synthesize.sh
```

## Input Contract

Output columns and artifacts are documented in:
- `../win-committee/references/data-contracts.md`
- `../win-committee/references/workflow.md`

## Guardrails

- Preserve score, majority opinion, and consensus fields from source matrix.
- Keep summary deterministic and easy to compare across reruns.
- If required inputs are missing, please interview the user to acquire the necessary inputs.

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

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Last commit (this folder)
Feb 5, 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-summary skill. Agent Mart. https://agentmart.dev/skills/andrew-tomago-win-summary (last updated 2026-10-24)