rlaope/ultraprompt
31 stars · Last commit 2026-09-15
Fable 5.1 solution tracing. Prompting skills so Opus, Sonnet, Haiku, Grok, Gemini and open-weight models work like Fable 5.1
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# ultraprompt 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, Sonnet 5, Haiku 4.5, 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 agents, syscalls, sequences, self- (8 core + 4 draft) storage, ... corrections ``` 1. **Curate cases.** A catalog of ~16 domains (frontend, UI design, high-traffic servers, distributed systems, ML training, kernel/quantization, agentic harnesses, CLI/Linux, compilers, storage engines, networking, security, data engineering, testing/legacy, concurrency, games) with per-case difficulty and a note on which reasoning mode each case is designed to provoke. Design-type, debugging-type, and optimization-type cases within the same domain stress different strategies on purpose. 2. **Run on Fable 5.1.** Each case is executed as a real Claude Code session, not a one-shot completion, so the model plans, calls tools, hits failures, and recovers. 3. **Extract the trace.** From the session transcript we keep the full reasoning surface: thinking blocks, the exact tool-call sequence, dead ends, and self-corrections — not just the final diff.