How I use Skills to make coding agents use proprietary libraries | Boston .

Members-Only

Recent Talks & Demos are for members only

Exclusive feed

You must be an AI Tinkerers active member to view these talks and demos.

February 23, 2026 · Boston

Skills: Proprietary Library Coding

Learn how to make coding agents use proprietary libraries with "Skills." This talk covers structuring and creating these lazy-loaded instructions, with a live demo of building an AI chat app.

Overview
Tech stack
  • Python
    Python: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.
    Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .
  • Cursor
    The AI-native code editor designed for high-velocity development through deep LLM integration.
    Cursor is a fork of VS Code that embeds AI directly into the development workflow while maintaining full extension compatibility. It leverages models like Claude 3.5 Sonnet and GPT-4o to power features such as Cmd+K for inline edits and Cmd+L for codebase-wide chat. By indexing local files, Cursor provides precise context for its predictive 'Tab' completions and multi-file 'Composer' mode. This setup allows engineers to move from high-level intent to functional code without leaving the editor or losing context.
  • Markdown
    Markdown is a lightweight markup language that converts plain text into structured HTML using simple punctuation.
    John Gruber released Markdown in 2004 to simplify web writing for non-coders and developers alike. It uses intuitive syntax (like # for headers or * for lists) to produce clean XHTML. This standard now powers documentation on GitHub, discussions on Reddit, and static sites via Jekyll. By prioritizing readability, Markdown ensures that a source document remains legible even before it hits a browser.
  • Skills
    The essential platform for building in-demand AI and technical capabilities: access free courses, skill badges, and industry-recognized Google Cloud certifications.
    Skills (Google) delivers targeted, high-impact technical training to close the global skills gap. The platform offers extensive no-cost options for all learning levels, including hundreds of courses focused on AI and technical fundamentals. Users earn shareable digital credentials: complete a certificate learning path for a full certification, or finish a series of courses to secure a Skill Badge, proving practical, technical expertise. This is the direct path for individuals and Google Cloud partners to validate knowledge and advance their careers with industry-recognized credentials.
  • Agent SDK
    Deploy autonomous, multi-step AI agents fast: the Agent SDK provides a lightweight, Python-first framework for LLM orchestration, tool integration, and enterprise-grade tracing.
    The Agent SDK is your production-ready framework for building complex, autonomous AI agents. It simplifies LLM orchestration (supporting OpenAI, Anthropic, and others) by managing the full execution loop, tool invocation, and multi-agent handoffs. Developers use Python functions to create tools (e.g., `WebSearchTool`, `CalendarTool`), which the agent autonomously calls to complete tasks. Key features include built-in tracing for debugging and optimization, plus configurable Guardrails for input/output validation. This is the efficient path to deploying reliable agents in production.

Related projects