Generative Skill Composition
Coding agents accumulate large skill libraries, and picking the right skills for a task has become the bottleneck. The usual options either dump the whole collection into context or retrieve skills with embeddings and rerankers, and both treat selection as a ranking problem rather than a joint plan. ---
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Composition as one joint decision: SkillComposer decides which skills, how many, and in what order all at once, instead of scoring skills independently and hoping the pieces fit together.
A constrained autoregressive decoder: A decoder over skill identifiers produces the full plan in a single pass, so dependencies between successive skills fall out of the generation naturally.
Strong gains at lower token cost: On SkillsBench with frontier models, it lifts pass rate well beyond the no-skill baseline, beats top-3 retrieval, and matches the gold-skill upper bound while using fewer prompt tokens.
Why it matters: As skill libraries keep growing, treating selection as generation rather than retrieval is what lets agents surface and sequence the right capabilities without drowning in their own toolbox.