SkillDES: Dependency- and Evidence-Aware Skill-Set Selection for Composite Tasks

Composite requests require complementary capabilities, yet ranking individual descriptions inherently fails to recover the required skill set. This mismatch is severely amplified by long documentation, uneven evidence distributions, and strict topological dependencies between subtasks. To address these challenges, we present SkillDES, a comprehensive retrieve-and-select framework that explicitly couples semantic task decomposition with dual-view candidate recall. To overcome semantic dilution in long documents, we introduce Long-Context Evidence Aggregation (LCEA), dynamically extracting bounded, node-specific complementary evidence for robust reranking. Furthermore, our dependency-aware beam search algorithm jointly balances skill compatibility, dynamic cardinality, and full capability coverage. Extensive evaluations across five diverse benchmarks demonstrate the effectiveness and robustness of our approach. Our system achieves a state-of-the-art complete-system SS-F1 of 68.4, representing a substantial improvement over the strongest baseline score of 51.2, while consistently outperforming existing retrieval, adaptation, and reranking methods and establishing a rigorous standard for composite capability selection. Our code is available at https://anonymous.4open.science/r/SkillDES.