rlaope/ultraprompt

README preview

# ultraprompt

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="assets/ultraprompt-logo-dark.png">
    <img src="assets/ultraprompt-logo-light.png" alt="ultraprompt" width="560">
  </picture>
</p>

Portable strategy skills for coding agents, to be distilled from the reasoning traces of a frontier coding model. (v0.2: baseline drafts, trace evidence pending — see [Status](#status) for what has actually landed.)

The pipeline: run a broad set of coding cases — frontend state machines, high-traffic backends, ML training loops, kernel optimization and quantization, agentic harnesses, low-level Linux — on **Claude Fable 5.1** via Claude Code; extract from the session transcripts how the model actually solves problems: what it explores first, how many hypotheses it keeps alive, what it accepts as evidence of "done", when it abandons an approach; distill those recurring strategies into English skill prompts that make a weaker agent — Claude Opus 5.5, Sonnet 5.5, Haiku 5.5, a GPT-class model, an open-weight model, anything that reads a system prompt — reason more like the stronger one. The output is not code. It is a set of carefully written prompts, to be grounded in trace evidence as case runs land.

## How it works

```
 (1) curate            (2) run              (3) extract           (4) distill          (5) publish
 ~16 case domains  →   Fable 5.1 via    →   full reasoning    →   recurring        →   skills/<axis>/
 frontend, servers,    Claude Code          trace: thinking       strategies along     SKILL.md
 ML, kernels,          sessions             blocks, tool-call     orthogonal axes      CASES.md

View full repository on GitHub →