Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning

['In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills. We start by setting up a robust SkillNet client with SDK and REST fallback support, then compare keyword search with semantic search to understand how skills can be found for different task requirements. From there, we install curated skills from GitHub, inspect their metadata, apply a quality gate across key evaluation dimensions, and visualize relationships between skills as a graph.

Finally, we build a skill-augmented agent planner that breaks a complex goal into subtasks, discovers relevant skills, filters them, and assembles an execution pipeline.', 'We begin by installing the required dependencies and preparing the basic environment for the SkillNet tutorial. This involves configuring API keys, model settings, GitHub options, and working directories to ensure the rest of the workflow runs smoothly. We also define a reusable banner function to keep the tutorial output organized and readable.

We initialize the SkillNet client and provide a REST fallback, so the tutorial remains usable even if the SDK does not work.', 'Next, we define helper functions to normalize search results and perform both keyword and semantic searches. We then compare a keyword search for PDF-related skills with a vector search for analyzing financial reports from documents. We create a curated list of useful SkillNet-compatible skills and expand it using the search results collected earlier.

We download selected skills from GitHub into a local skills directory when the SDK is available.', 'We inspect the installed skills by searching for their SKILL.md files and reading their metadata. We parse the front matter to extract useful information, such as the skill name and description. We then print a clean summary of each installed skill to understand what has been added locally.

We evaluate skills across five quality dimensions: safety, completeness, executability, maintainability, and cost awareness. We apply a quality gate to determine which skills meet a minimum score threshold, using mock scores when an API key is unavailable.', 'We analyze relationships between skills and visualize them as a Skill Graph using NetworkX and Matplotlib. We build a skill-augmented agent planner around a complex scientific discovery goal.

We decompose the goal into ordered subtasks, identify relevant skills for each step, and map those skills to an execution pipeline. We finish by printing a recap of the full workflow, including search, installation, inspection, evaluation, graph analysis, and planning.', 'In conclusion, we created a complete SkillNet workflow that moves beyond simple skill search and demonstrates how skills can support structured agentic systems. We saw how SkillNet helps us discover useful capabilities, evaluate their quality, understand their relationships, and connect them to real task planning.