juanallo

juanallo / six-hats-skill

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A structured decision-debate skill for running Edward de Bono-style six hats sessions with an AI agent. It walks a topic through facts, intuition, upside, risk, alternatives, and final moderation so you get a practical recommendation instead of a loose brainstorm.

26
6
100% credibility
Found May 02, 2026 at 24 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository is a skill for AI agents that runs structured 'six hats' debates on user-provided topics to provide balanced decision-making insights in markdown reports.

How It Works

1
🕵️ Discover Six Hats

You find this helpful tool for making better decisions by having an AI debate your ideas from different angles.

2
📥 Add to Your AI Helper

You place the skill folder into your AI assistant's special toolbox so it's ready to use.

3
💭 Pick Your Topic

You choose a big decision like a career move or project plan that you want to think through carefully.

4
🎩 Start the Hats Debate

You tell your AI to run a six hats session on your topic and where to save the results, then watch it go through facts, feelings, benefits, risks, ideas, and a final summary.

5
⏳ Follow the Rounds

The AI takes turns wearing each thinking hat over a few rounds, building a full picture just for you.

📄 Get Your Report

You open the new file full of insights, agreements, next steps, and a clear recommendation to guide your choice.

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AI-Generated Review

What is six-hats-skill?

Six-hats-skill is a Python skill for LLM agents like Claude or Cursor that runs Edward de Bono-style six hats sessions on any topic. It sequences perspectives—facts, intuition, upsides, risks, alternatives, and final moderation—to deliver structured output github-style debates as markdown files, swapping loose brainstorms for practical recommendations. Clone it into your agent's skills directory, prompt with a decision like "six hats debate on migrating to GraphQL, save to ./decisions," and get a timestamped file with synthesis and next steps.

Why is it gaining traction?

It enforces a bono-style agent flow with github llm structured output, building each hat on the last for balanced decision-debate instead of rambling chats. Developers hook on the raw value: pressure-tested facts, alternatives, and final recs on real stakes like tech stacks or career pivots. No fluff—just structured text that beats ad-hoc prompting.

Who should use this?

Engineering leads evaluating architecture shifts like REST to GraphQL, frontend devs debating React staying power versus alternatives, or PMs hashing product directions. Solo makers or teams needing edward de bono hats for career moves, strategy plans, or risk checks before committing.

Verdict

Solid docs and examples make it usable now, despite 16 stars and 1.0% credibility score signaling early maturity. Grab it if you run agent skills and want structured output openai github vibes for decisions—run one debate to see the edge over vanilla brainstorming.

(178 words)

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