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COBRA-Skills

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-09-12 / skill-finderSkill optimization via contextual bandits cut optimization cost 55-58% using only 50 examples per benchmarkCOBRA-Skills treats agent skill optimization as budgeted sequential optimization over a candidate space that keeps evolving, pairing contextual-bandit-guided prioritization with evidence-grounded skill evolution so evaluation budget goes to promising or informative candidates rather than the whole population. Across six agent benchmarks and three target models it achieved the strongest average performance among compared methods while reducing optimization cost 55-58% against SkillOpt, on 50 unique optimization examples per benchmark. It stayed robust when the agent harness changed and worked when the target model itself generated and refined the skills, which removes the need for a separate stronger teacher.

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COMPETES WITH
SkillOpt
COBRA-Skills achieved 55-58% cost reduction compared to SkillOpt
Source finding

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arXiv 2609.11682 — COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization

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