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joeseesun / qiaomu-heavyskill

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Deep multi-perspective reasoning via parallel isolated subagents + Codex deliberation host, with final Markdown + HTML report output. Use for complex

69
5
69% credibility
Found May 17, 2026 at 69 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This project is a thinking enhancement tool for AI assistants. When you ask a complex question, it helps your AI think more carefully by exploring multiple independent reasoning paths simultaneously, then combining what it learned from each path into one comprehensive answer. It's designed for people who want their AI assistant to go beyond surface-level responses and really think through difficult problems.

How It Works

1
🤔 You have a tricky question

You're working on something complex and want your AI assistant to really dig deep into the answer.

2
🔌 You enable the thinking skill

You turn on the Deliberate skill in your AI assistant so it can approach your question from multiple angles.

3
🧠 Your AI thinks harder

Instead of giving one quick answer, your assistant explores several different reasoning paths at the same time.

4
🔄 It compares and combines ideas

Your assistant looks at what each path discovered and figures out the best overall answer by learning from all of them.

✨ You get a thorough answer

You receive a well-thought-out response that considers different perspectives and gives you confidence in the solution.

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AI-Generated Review

What is qiaomu-heavyskill?

This is a reasoning framework that tackles complex problems by spinning up multiple isolated thinking agents in parallel, then synthesizing their conclusions through a deliberation process. The output is a structured Markdown and HTML report. Based on the interface definition, it exposes itself as an agent skill compatible with Claude and generic LLM adapters. The system uses a manual activation mode, meaning you explicitly trigger the heavy reasoning when needed rather than it running automatically.

Why is it gaining traction?

Multi-agent reasoning is having a moment. Developers are drawn to the idea of letting several " perspectives" tackle a problem simultaneously rather than wrestling with a single linear chain of thought. The deliberation synthesis step is the differentiator--rather than just aggregating responses, it actively deliberates across traces. The Markdown and HTML report output gives you something concrete to share or archive, which matters when reasoning-heavy tasks need stakeholder buy-in.

Who should use this?

This targets developers working on complex analytical tasks where single-pass LLM responses fall short. Legal researchers parsing dense contracts, architects evaluating competing technical proposals, or data scientists debugging subtle model failures come to mind. If you find yourself repeatedly prompting a model to "think harder" or running multiple iterations to get quality answers, this formalizes that workflow. It's not a casual tool--the interface suggests you know what you're doing.

Verdict

With a credibility score of 0.7% and only 69 stars, this is an early-stage project with minimal community validation. The binary README prevents you from evaluating documentation quality before trying it. Proceed with caution: start with a small, low-stakes query to test whether the deliberation actually adds value for your use case before committing it to serious work.

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