K-Dense-AI

K-Dense-AI / mimeo

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Mimeograph an expert into a SKILL.md or AGENTS.md for your agent.

www.k-dense.ai
16
1
100% credibility
Found Apr 23, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Mimeo researches public writings, talks, and interviews of experts to automatically generate skill files that teach AI coding agents their thinking patterns.

How It Works

1
💡 Discover mimeo

You learn about a helpful tool that captures how famous thinkers reason, turning their ideas into guides for your AI coding helper.

2
🛠️ Get it ready

You easily set it up on your computer and connect it to smart online search and thinking services.

3
🎯 Name your expert

You enter the name of an expert like Feynman or Naval Ravikant, and it handles any mix-ups by asking for a quick description if needed.

4
🔍 Gathers insights

It scours the web for their writings, talks, interviews, and videos, pulling together the best sources.

5
🧠 Extracts wisdom

It reads everything, picks out key principles, quotes, frameworks, and habits, then organizes them neatly.

6
📁 Creates skill files

You get polished files like SKILL.md or AGENTS.md, complete with references, ready to drop into your AI setup.

🚀 AI levels up

Your coding assistant now reasons with the expert's mindset, making smarter decisions on every project.

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Star Growth

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AI-Generated Review

What is mimeo?

Mimeo is a Python CLI tool that mimeographs an expert's thinking—pulling from essays, talks, podcasts, and papers via Parallel Search—into ready-to-use SKILL.md or AGENTS.md files for AI coding agents. Name someone like Naval Ravikant or Jensen Huang, tweak flags like `--format skill` or `--mode full` for audio transcription, and it outputs structured principles, frameworks, quotes, and references your agent can load directly. It solves the grind of manually distilling decades of scattered content into agent prompts.

Why is it gaining traction?

Unlike manual synthesis or basic RAG scrapers, mimeo auto-disambiguates names, verifies quotes against sources, clusters ideas across 40+ docs, and even critiques the output with an adversarial LLM pass—delivering polished, attributable agent skills in minutes. The hook is its end-to-end pipeline: discovery to authoring, with caching for cheap re-runs and optional deep research for richer results. Developers dig the production-ready anatomy matching Anthropic's skill-creator format.

Who should use this?

Agent builders crafting libraries of on-demand SKILL.md files for domain experts, like physics sims channeling Feynman or startup codebases with Naval's heuristics. Teams wanting always-on AGENTS.md to default their daily coding to thinkers like Darwin on hypothesis-building. Python devs experimenting with expert-infused agents in tools like Cursor or Claude.

Verdict

Try it if you're building agents—CLI is intuitive, docs solid, tests near-perfect—but at 16 stars and 1.0% credibility, it's early; expect tweaks as it matures. Solid for prototypes, monitor for scale.

(198 words)

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