Open Source Project of the Day (#84): SkillLens - Microsoft's 'Microscope' for the AI Agent Skill Lifecycle

Microsoft Research has released SkillLens, an open-source framework designed as a "microscope" for analyzing how AI agents absorb and utilize skills. The framework provides a complete pipeline for extracting, standardizing, and validating model-generated skills from raw execution trajectories, using techniques like parallel extraction and hierarchical merging. Accompanied by the paper *From Raw Experience to Skill Consumption*, SkillLens enables developers to systematically compare agent performance between skill-injected and baseline runs through a standardized four-stage experimental process.

"It's not enough to give an Agent skills; we must understand how those skills are actually 'absorbed' by the model." This is the 84th article in the "One Open Source Project per Day" series. Today, we are introducing SkillLens from Microsoft. If the previously featured SkillOpt https://home.wonlab.top/en/blog/open-source-projects/118-skillopt is an execution strategy for boosting AI skills, then SkillLens is the "microscope" for studying the evolutionary process of those skills. It provides a scientific analysis framework to help researchers and developers understand how a skill summarized by an AI actually impacts the execution efficiency of another AI. SkillLens is an open-source framework from Microsoft Research dedicated to the systematic study of "model-generated agent skills." It provides a complete pipeline covering everything from trajectory loading and skill extraction to inference validation. Released alongside the paper From Raw Experience to Skill Consumption , this project is one of the most authoritative tools in the field of AI Agent skill research. sequential baseline and the sophisticated parallel method per-trajectory extraction with hierarchical merging .Converts raw trajectories from diverse sources e.g., complex SWE-bench debugging logs or simple ALFWorld game traces into a unified JSON Schema, enabling large-scale batch skill extraction. A key technology within SkillLens is its parallel extraction approach. It analyzes individual trajectories to distilled specific "modes" and uses hierarchical merging algorithms to generate high-level, generalized skill set.json files. Using the simple skilllens infer command, developers can easily compare Agent success rates between "skill-injected" and "base" runs. SkillLens standardizes every experiment into four distinct stages: This rigorous scientific process serves as an excellent reference for developers looking to integrate "self-evolving" capabilities into their own AI products. While SkillOpt focuses on the "how," SkillLens explains the "why." As a vital component of Microsoft's Agent research ecosystem, SkillLens reveals the deep underlying mechanisms by which AI learns from its own experience and translates it into executable knowledge. For developers seeking peak performance in Agent systems, the empirical evaluation methods provided by SkillLens are an indispensable navigation beacon. Find more useful knowledge and interesting products on my Homepage