The authors tackle a practical problem in agent systems: when an AI agent needs to retrieve skills from a large library, returning a flat list of individual skills forces the agent to figure out which one to run first, which are helpers, and how they fit together. Their "Group of Skills" method instead packages retrieved skills into a structured bundle with explicit labels—Start (the entry point), Support (helper skills), Check (preconditions), and Avoid (failure modes)—so the agent receives ready-to-execute context rather than raw pieces. Tests on two benchmarks (SkillsBench and ALFWorld) show this structured retrieval keeps important requirements visible even under tight memory budgets and often speeds up task completion.
Main takeaways:
- Traditional skill retrieval returns atomic skills or dependency bundles but leaves the agent to infer execution order and roles, which wastes inference cycles and risks missing preconditions.
- GoSkills builds a typed graph of skills, clusters them around "anchor" skills, and outputs a fixed contract with four fields: Start, Support, Check, and Avoid.
- The method works at inference time—no retraining of the agent, the skill payloads, or the execution environment.
- Experiments show better coverage of requirements under memory constraints and improved task success and runtime compared to flat-list baselines.
- The approach is modular: you get structured context without changing how skills are stored or how the agent executes them.