Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents
SkillTTA introduces a pragmatic shift in how LLM agents adapt to novel tasks without retraining. Rather than maintaining static skill libraries, the method synthesizes task-specific guidance by retrieving and contextualizing relevant training trajectories at inference time. This context-only adaptation strategy sidesteps parameter updates entirely, reducing deployment friction while delivering measurable gains: 27% improvement on spreadsheet tasks and 26% on code generation benchmarks versus fixed skill baselines. The approach signals growing maturity in prompt-based agent customization, where retrieval and synthesis replace fine-tuning as the primary lever for task specialization.62
