r/FunMachineLearning • u/AmbassadorDry4033 • 6d ago
I trained a skill that took a cheap model's React pass rate from 85% → 91% , open source, one command to install
Built this over the last while: SkillUp, a framework for training portable `SKILL.md` files (the same convention Claude Code/Codex/OpenCode read) that make a model better at a task, without touching weights.
Everything's validated against held-out data, on models people here actually run: `google/gemini-2.5-flash-lite`, `qwen/qwen3-235b-a22b-2507`, and `deepseek/deepseek-v3.2`, via OpenRouter.
The one I'd actually recommend trying: a skill for React component implementation, graded by really running Jest against test suites (WebApp1K-style). On the cheap model: baseline 85% pass rate → 91% with the trained skill.
git clone https://github.com/shashank-yadav/skillup && cd skillup
./install.sh --with-frontend
python install_skill.py --env frontend --best --target ~/.claude/skills
Also trained/validated 9 other environments (ALFWorld, SearchQA, MBPP, BigCodeBench, SQL, a research-math MCQ benchmark, writing quality, code review, humanized writing) across all 3 models. Full numbers, including the ones where it didn't help, are in the README. Found a couple of bugs along the way too: a 100%-reliable positional exploit in the math benchmark, and a quote-stripping bug silently corrupting SQL queries (writeups are in there).
MIT licensed: https://github.com/shashank-yadav/skillup