SkillLens organizes skills into a four-layer hierarchy (policies → strategies → procedures → primitives) and retrieves them at mixed granularity to balance relevance and cost. Given a task, it retrieves seed skills, expands via random walk over the skill graph, then uses a verifier to decide for each node whether to accept, decompose, rewrite, or skip. This lets agents reuse compatible subskills directly while adapting only mismatched parts. The system also refines skills and the verifier over time. Results show up to 6.31 percentage-point accuracy gains on bug localization and improved agent success on ALFWorld.
Main takeaways:
- Hierarchical skill graph (four layers from high-level policies down to primitives) enables mixed-granularity retrieval
- Expand via degree-corrected random walk, then verify each node: accept whole, decompose, rewrite, or skip
- Mixed-granularity adaptation has sublinear cost when mismatches are sparse (theoretical result)
- Evolutionary update rule monotonically improves validation objective to local optimum
- Consistent gains over flat skill baselines: +6.31pp on bug localization, +6.31pp agent success (45% → 51.31%) on ALFWorld