Skill pass rates jump from 33.6% to 88.0% — with zero gradient steps

Notes on Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO (arXiv:2604.27488) — Yu Tian, Jiawei Chen, Lifang Zheng, Ming Tao, Xinyi Zeng, Zhaoxia Yin, Hang Su, Xian Sun · April 2026

Note published July 23, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

AI-assisted notes · reviewed by SkillFed Skill evolution

Skills-Coach puts a skill through a four-stage self-improvement loop. First, a task generator reads the skill's own specification and writes a test suite of standard, advanced, and boundary-case tasks. Then a Training-Free GRPO optimizer rewrites the skill's instructions and code in parallel — generating multiple variants and scoring them against each other, with no gradient updates and no backprop through model weights. A Comparative Execution Module runs the original and the optimized version on identical tasks inside isolated environments; a Traceable Evaluation Module then grades both against 51 explicit criteria spread across 8 dimensions, with the pass line set at 70% of the maximum score. Execution comes in two modes — a fast 'virtual' mode that estimates completion from keyword matches and hash-seeded pseudo-randomness, and a 'real' mode that actually runs the skill and inspects its output files, logs, and errors. To test the whole pipeline, the authors built Skill-X, a 48-skill benchmark drawn from the Anthropic, Clawhub, and Vercel Labs skill libraries — 29 instruction-only, 19 shipped with code.

Across all 48 skills, average task score rose from 0.37 to 0.84 and pass rate climbed from 33.6% to 88.0%, using just 3 optimization epochs and 12 generated tasks per skill. Code-bearing skills started furthest behind — a 26.97% pass rate — but gained the most in relative terms, closing to 82.89%; instruction-only skills moved from 37.93% to 91.38%. Twenty-three of the 48 skills gained at least 0.5 in score. Four went from a dead 0.0 to a perfect 1.0, Browser and MCP-Builder among them, and only one skill improved by less than 0.1 — a single near-miss inside an otherwise near-universal lift.

Key numbers

Pass rate across 48 skills	33.6% → 88.0%
Average task score, relative gain	0.37 → 0.84 (+127%)
Code-inclusive skills, starting pass rate	26.97%
Skill-X benchmark size	48 skills (29 instruction-only / 19 code)
Training tasks used per skill	12 (3 epochs, 8 held-out test tasks)

What this means for skill authors

The real finding here is that skill quality is cheap to fix once it's measurable. Skills-Coach doesn't need a reward model or a labeled dataset of thousands of trajectories — it needs a way to generate test tasks from the skill's own spec and a way to score the result. From there, an LLM iterates on its own instructions and code like an unusually literal editor. For anyone authoring a skill for a team library or a marketplace, the practical read is blunt: an unoptimized skill.md is plausibly leaving 40-50+ points of pass rate on the table, and closing that gap here took generated test cases and a few rewrite epochs, not a training run.

The split between code-bearing and instruction-only skills matters more for catalog curators than for solo authors. Code-inclusive skills started lowest and improved the most in relative terms — read that as: code paths fail for mechanically fixable reasons (missing input validation, uncached calls, unhandled errors) more often than prose paths do. That's exactly the class of defect worth catching before a skill reaches a catalog rather than after an agent hits it live — the same logic behind screening skills with more than one independent tool instead of trusting a single pass.

One caveat worth carrying forward: the paper's 'virtual mode' scores completion from keyword presence and a hashed pseudo-random check, not real execution, so its cheaper numbers are proxies. The real-mode results are the ones that actually tell you whether a given optimized skill is production-ready.

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Related notes

References

Tian, Y., Chen, J., Zheng, L., Tao, M., Zeng, X., Yin, Z., Su, H., & Sun, X. Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO. arXiv:2604.27488 (2026).