Compositional Skill Routing
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Real tasks rarely map to a single skill. They usually need several skills composed together, yet most skill routing still treats the problem as picking one tool from a library. This work formalizes Compositional Skill Routing, where an agent must select and sequence multiple reusable skills from large libraries to satisfy a complex query, and introduces SkillWeaver, a decompose, retrieve, and compose pipeline built around it.
A three-stage pipeline: SkillWeaver decomposes a query into sub-tasks with an LLM, matches each sub-task to a skill using a bi-encoder with FAISS indexing, and then performs dependency-aware planning to assemble an executable plan.
A realistic benchmark: The authors release CompSkillBench, a benchmark of 300 compositional queries over 2,209 real MCP server skills spanning 24 functional categories, so routing is tested against actual tool ecosystems rather than toy libraries.
Decomposition is the bottleneck: Task decomposition quality emerges as the primary limiting factor, and Iterative Skill-Aware Decomposition, which feeds retrieval information back into the decomposition step, lifts accuracy from 51.0% to 67.7%.
Why it matters: As agent skill libraries scale to thousands of entries, single-tool routing stops being enough, and treating routing as a compositional planning problem is what lets agents handle genuinely multi-step requests.
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