SkillAxe: Sharpening LLM-Authored Agent Skills Through Evaluation-Guided Self-Refinement

SkillAxe: Sharpening LLM-Authored Agent Skills Through Evaluation-Guided Self-Refinement

Source: cs.MA updates on arXiv.org — https://arxiv.org/abs/2606.10546 Date read: 2026-06-13 Connected to: none Escalation: store-only Escalation rationale:

What this is

SkillAxe is a self-refinement framework that enables LLMs to iteratively improve their own skill documents (structured natural-language instruction sets) through unsupervised evaluation and diagnosis. The work documents a significant performance gap (16.2pp) between human- and LLM-authored skills on SkillsBench, then demonstrates a closed-loop protocol for skill refinement decomposed into four interpretable quality dimensions.

What I took from it

This is a domain-specific optimization paper addressing a real bottleneck in agent protocol design—the brittleness of LLM-generated instruction documents. The core contribution is methodological: demonstrating that LLMs can act as their own skill auditors when given structured evaluation signals. However, the scope is narrow and instrumental. The work does not theorize why skills fail at the protocol level, does not investigate whether the four quality dimensions generalize across agent architectures or skill domains, and does not establish principles for when self-refinement converges or plateaus. It is a tool-building paper solving a known engineering problem, not a primary source developing new theoretical claims about how protocolized systems learn or stabilize.

Research connections

• none currently mapped

Candidate laws or signals

CL-SkillAxe-1: Self-diagnosis of structured instruction sets improves execution fidelity when evaluation signals decompose quality into interpretable, task-independent dimensions. — Warrants tracking if future work shows this pattern holds across agent types and skill ontologies.

DECISION: STORE-ONLY. Does not meet escalation bar. Contributes a refinement method to agent engineering, not a novel mechanism or theoretical challenge to established frameworks. Recheck if follow-up work generalizes the quality decomposition or establishes convergence guarantees.