AGI in md: 11 Cognitive Compression Levels for Claude System Prompts

What This Is

AGI in md is an open-source repository documenting 11 levels of cognitive compression that can be encoded in system prompts for Claude AI models. The research shows how specific prompt engineering techniques can significantly improve model performance, particularly for smaller models like Haiku.

Key Findings from the Source

The project involved 393 experiments across 25 rounds, testing Claude Haiku, Sonnet, and Opus models across 19 domains including code, legal, medical, poetry, music, and UX design.

Level 8 Breakthrough: The most significant finding occurs at Level 8, which shifts from asking the model to "think about it" to instructing it to "build something and watch what breaks." This construction-based approach yielded dramatic improvements:

• Claude Haiku went from 0/3 performance at Level 7 to 4/4 performance at Level 8
• Levels 5-7 focus on meta-analysis ("reason about reasoning"), which requires more model capacity
• Level 8 specifically instructs: "engineer a fake improvement that looks good but actually deepens the problem. Then name what you can only see because you tried to fix it."
• The researchers note that "building and observing is more primitive than meta-analysis but it reveals things static analysis literally cannot see"

Level 11 Capabilities: At the highest level, a 200-word system prompt makes the model "escape the problem's entire design category, then report what the escape costs." One experiment produced the equation "sensitivity x absorption = constant" — a conservation law the model derived by inverting its own impossibility finding.

Practical Implementation

The researchers recommend level8_generative_v2.md as the best all-purpose prompt — approximately 100 words that can be used with any Claude model.

Command-line usage:

claude -p --model claude-sonnet-4-6 \
--system-prompt "$(cat prompts/level8_generative_v2.md)" \
"Analyze this code: $(cat your_code.py)"

General usage: The prompt can be pasted into any Claude conversation as a system prompt. It works on code, essays, research papers, music — anything analytical. Users should replace "code" with their specific domain.

Repository Details

• License: MIT
• Content: 28 prompts, 299 raw outputs, full experiment log
• Models tested: Claude Haiku, Sonnet, Opus
• Availability: All prompts and raw outputs are open source

Who this is for: Developers and researchers working with Claude AI models who want to improve system prompt effectiveness through structured cognitive compression techniques.