SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents

Key points

• Independently authored skills can impose incompatible requirements, competing preferences, and redundant instructions when jointly loaded by an LLM agent.1
• A type-scope gate selects candidate interactions, while selective fallback revisits pairs potentially missed by structural pruning.1
• A classifier identifies interaction types, and an adversarial verifier re-examines uncertain judgments.1
• Source-linked checks guide synthesis and compression, and metadata synthesis supports discovery of the merged skill.1

Sources (1)

• [1]SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:30 AM

  We propose SkillFusion, an offline compiler that reconciles these interactions into a reusable merged skill.

  Independently authored skills can impose incompatible requirements, competing preferences, and redundant instructions when jointly loaded by an LLM agent.

Extractive summary: sentences quoted from the sources.