Self-Authored Skills Crashed One Model's Accuracy From 78% to Under 1%

Notes on EvoClawBench: Can Agents Learn Reusable Skills from Their Own Runs? (arXiv:2607.09711) — Zhiyuan Peng, Xin Yin, Chenhao Ying, Zhe Cui, Zixiang Ding, Zhenhua Liu, Jiang Wu, Yuan Luo · June 2026

Note published July 25, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

AI-assisted notes · reviewed by SkillFed Skill evolution Bridge: evolution × benchmarks

EvoClawBench asks a narrow, closed-loop question: can an agent turn evidence from its own task runs into a reusable skill that makes a fresh run better, after paying the cost of authoring it? The benchmark spans 100 tasks (502 sub-problems) across coding, data, office, security, operations, and domain-document workflows — finance, legal, healthcare, procurement and more. Every task runs through three conditions: a direct baseline with no skill involved, PreSkill (write a skill before ever attempting the task, then solve using only that skill), and PostSkill (attempt the task once, then summarize a skill from that run's evidence and solve again in a fresh workspace). Two agent runtimes, OpenClaw and nanobot, each ran five model backends — GPT-5.4, GPT-5.4 mini, Qwen3.6-Plus, DeepSeek-V4-Pro, and MiniMax-M2.7 — through all three conditions on identical tasks.

The choice of runtime scaffolding swamps the choice of model: OpenClaw stayed under 20% baseline accuracy across every backend, while nanobot ranged from 56.45% to 96.13% on the same tasks. Adding self-authored skills doesn't move scores in one direction. Nanobot's MiniMax-M2.7 rose from 90.97% to 94.50% under PostSkill, and its GPT-5.4 held above 96% throughout. But nanobot's DeepSeek-V4-Pro collapsed from 77.77% baseline to 4.80% under PreSkill and 0.99% under PostSkill, and OpenClaw's GPT-5.4 fell from 18.63% to 1.14% under PostSkill. The number of skills generated tracked none of this — batches of 20-plus skills produced both wins and wipeouts. Even the wins aren't free: skill workflows ran at token-efficiency ratios as low as 0.21-0.40 against baseline (roughly 2.5x to nearly 5x the token spend) for a slightly lower score. Wall-clock time only breaks even after 9 to 14 reuses of the same skill.

Key numbers

Benchmark scale	100 tasks / 502 sub-problems
OpenClaw baseline ceiling (all 5 models)	under 20%
nanobot DeepSeek-V4-Pro: Baseline→PreSkill→PostSkill	77.77% → 4.80% → 0.99%
nanobot MiniMax-M2.7 under PostSkill	90.97% → 94.50%
OpenClaw+Qwen3.6-Plus PostSkill token efficiency	0.30x baseline (~3.3x tokens)

What this means for skill authors

The sharpest result here isn't the headline collapse, it's the ablation buried in the appendix. On a 4-task subset, a real self-authored skill lifted mean score by 4.05 points — but a deliberately empty scaffold lifted it by 19.84 points, and an irrelevant skill by 1.52. The authors are direct about what that means: some of what looks like 'skill content working' is actually the reuse prompt, the context reset, and ordinary run-to-run variance. For anyone building a skill authoring pipeline, that's a warning against grading a skill on the run that produced it. A skill only earns trust from a fresh, independent execution — ideally several — not from the transcript it was distilled out of.

That's also the practical argument for treating skill provenance as a first-class signal rather than an afterthought. A skill an agent writes for itself, from one imperfect run, is unvalidated code shipped straight from an untested source — and this paper documents that code actively wrecking accuracy in specific runtime-model pairings, with no visible warning sign beforehand. Vetting skills before they're trusted — the way a catalog reviews an artifact once, independent of the agent that produced it — is a different bet than letting every agent regenerate and re-risk its own skill library on every task. SkillFed's dual-tool scanning approach to a shared catalog is built on exactly that distinction; this paper is evidence for why the distinction matters, not a citation of any SkillFed number.

There's a second, quieter lesson about cost. Even the skill runs that didn't hurt accuracy burned several times the tokens of the baseline. The authors calculate that wall-clock time alone needs 9 to 14 repeated uses of a skill before the authoring investment pays for itself — before counting token cost or dollars, which never broke even on their subset. A skill worth keeping has to clear a quality bar and an amortized-cost bar. Skills authored once and reused across many callers, rather than reauthored per agent per task, are the version of this that actually pays that cost down.

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Related notes

References

Peng et al., "EvoClawBench: Can Agents Learn Reusable Skills from Their Own Runs?", arXiv:2607.09711 (2026)
Jimenez et al., "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" (2024)
Zhou et al., "WebArena: A Realistic Web Environment for Building Autonomous Agents" (2024)
Mialon et al., "GAIA: A Benchmark for General AI Assistants" (2024)