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.
import sys, subprocess
def _pip(*pkgs):
subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False)
print("Installing dependencies (skillnet-ai, networkx, matplotlib, requests)...")
