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Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python

This tutorial demonstrates building a complete skill-based agent system for LLMs, where modular capabilities are structured like an operating system. It covers defining reusable skills, a central registry, dynamic orchestration via tool calling, multi-step reasoning, hot-loading, and an observability dashboard.

SourceMarkTechPostAuthor: Sana Hassan
Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Define skills as self-contained modules with metadata, schema, and execution logic.
  • A SkillRegistry serves as the central catalog, supporting retrieval by category/tag and exposing tools to LLMs.
  • Skills can be composed (e.g., ResearchReportSkill) and hot-loaded at runtime via SkillLoader.
  • An LLM-driven agent dynamically routes tasks to appropriate skills through a ReAct loop.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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