{"id":7042803,"date":"2026-08-20T13:32:08","date_gmt":"2026-08-20T13:32:08","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/build-ai-agents-with-langchain-skills-generate-ppts-excel\/"},"modified":"2026-08-20T13:32:08","modified_gmt":"2026-08-20T13:32:08","slug":"build-ai-agents-with-langchain-skills-generate-ppts-excel","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7042803","title":{"rendered":"Build AI Agents with LangChain Skills: Generate PPTs & Excel"},"content":{"rendered":"


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Ever wondered how ChatGPT, Gemini, and other chat interfaces generate PDFs, PowerPoints, and more when all they have under the hood is an LLM? The trick isn\u2019t a smarter model. It\u2019s something simpler:\u00a0skills<\/mark> which are instructions an agent loads only when needed.<\/em><\/p>\n

Next, let\u2019s explore how skills work using LangChain and how they can make your own agents more capable, flexible, and efficient.\u00a0In this article,<\/strong>\u00a0we\u2019ll break down the concept and build a practical understanding of how skills transform agentic workflows.<\/p>\n

About LangChain, Middleware, and Skills<\/h2>\n

LangChain<\/strong> is a framework for building LLM-powered systems, such as agents, chains, or retrieval pipelines. Moreover, the framework helps with model calls, tools (pre-built and custom), and memory. Consequently, its \u2018create_agent\u2019 helper wires up a model, a set of tools, and a system prompt into a working agent in a few lines.<\/p>\n

Middleware functionality and Skills<\/h3>\n

Middleware<\/strong> sits between the agent and the model on every turn. It can rewrite the request before the model sees it, inspect the response before it returns, or inject extra tools, all without touching the agent\u2019s core logic. Developers mirror the idea of HTTP middleware here.<\/p>\n

Skills <\/strong>build on top of middleware. A skill is a self-contained set of instructions the agent loads only when it\u2019s relevant, usually via a load_skill<\/code> tool. The agent sees a short list of the available skills and pulls in the full detail only for the skill it needs. For example, you can treat them as specialized sets of prompts. This is a better alternative than stuffing every possible instruction into one giant system prompt, which can be expensive, as the model must read all of it every time.<\/p>\n

Building a specialized agent<\/h2>\n

Finally, let us now make a specialized agent with two skills: one that writes PPT decks and one that writes Excel reports. Similarly, both skills live as SKILL.md<\/code> files and hand off to a real tool that saves the file. Let\u2019s go step-by-step.<\/p>\n

Pre-Requisites<\/h3>\n