SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time

2026-08-07

AI Summary

This work addresses the semantic misalignment between retrieved general-purpose skills and the current task, environment, or other skills during execution—where skills are semantically relevant but suffer from execution-level mismatches. To resolve this, the paper proposes SkillAligner, a framework that treats retrieved skills as tunable drafts at inference time without requiring additional training. SkillAligner performs a one-shot joint adaptation to simultaneously customize skills for the target task, align their interfaces, and coordinate multi-skill interactions by resolving dependencies, eliminating conflicts, and removing redundancies, thereby producing a compact and unified execution plan. Experiments demonstrate that SkillAligner significantly improves task success rates across diverse agent benchmarks and model backbones, effectively mitigates performance degradation caused by skill integration, and reduces inference overhead.

Abstract

General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills. We formalize this problem as the skill--execution misfit. To address it, we propose SkillAligner, a training-free execution-time skill adaptation framework that treats retrieved skills as adaptable drafts rather than fixed instructions. Before execution, SkillAligner performs a one-time joint adaptation that specializes useful skill fragments to task requirements, aligns their procedural assumptions with the available execution interface, and composes the resulting guidance by resolving dependencies, conflicts, and redundancy across skills. The adapted content is consolidated into a compact execution guide and reused throughout the subsequent trajectory. Extensive experiments across diverse agent benchmarks and model backbones show that SkillAligner substantially improves task performance over existing skill-use baselines, reduces skill-induced regressions at the instance level, and lowers total inference cost.

Problem

• skill-execution misfit
• retrieved skills
• execution utility
• procedural assumptions
• language agents

Innovation

• skill adaptation
• execution-time alignment
• language agents
• reusable procedural knowledge
• training-free framework