Extending AI with Agent Skills 2026

Explore why skills matter for LLMs and what agent skills bring to the table. Understand the context problem, why it exists, and how skills address it for effective use.

Adopt the lighthouse model of LLMs to understand how prediction travels through memory and context, guiding inference with lighthouses and beacons, while managing drift and risk.

Understand the context window of a large language model as its working memory—the finite amount of content it can process, including input and output.

Explore how context windows and the lighthouse model reveal why skills are essential for injecting context and guiding LLMs, detailing window size and its limitations.

Explore how system prompts, including hidden prompts, guide AI agents by combining built-in rules with added context to shape how the LLM behaves and responds.

Explore how filling the context window affects performance, how context rot causes drift and hallucination, and how starting a new session helps mitigate degradation in large-context LLMs.

Explore the structure of an agent skill and learn when to use it and how to build it properly as a foundation for extending ai with agent skills.

Explore markdown, a lightweight markup language that marks up plain text to convey meaning for llms. Learn to use headers, bold, italics, and lists to structure content and provide context.

Explore the anatomy of a skill, how to use and build one, within the open standard Agent Skills and its SKILL.md file and directory structure.

Learn how YAML front matter provides metadata for agent skills, guiding the LLM to activate PPTX skills for reading and editing presentations.

Describe how skill instructions guide LLM tasks, optimize context window usage by splitting content into referenced files, and use step-by-step instructions, patterns, and edge cases to enable effective agent skills.

Learn how agent skills use scripts to enable the LLM to run executable code via an external agent, emphasizing self-contained scripts, clear dependencies, robust error handling, and security considerations.

Package knowledge as an agent skill by organizing a folder with a skill.md and assets like templates, images, and data files. These resources provide the llm context for reusable tools.

Explore how agents implement and integrate skills under the hood, revealing the anatomy of a skill with cloud code, cursor, and codex, to improve reliability and debugging.

Explore progressive disclosure by introducing information gradually and revealing only what is needed during an LLM session, conserving the context window and reducing cost and context rot within skills.

Discover how to enable an ai agent to recognize skills by scanning configured folders for skill.md, registering each skill into a unified registry, and informing the llm about available skills.

Load metadata passes YAML front matter from skill.md to the LLM at startup, creating a lightweight skill index in the system prompt and enabling progressive disclosure.

Match user prompts to a skills registry by aligning tasks with precise descriptions, enabling PDF processing, data analysis, and extract text and tables from PDFs, fill forms, and merge documents.

Activate skills by loading full skill instructions into the LLM's context window, enabling the agent to know when to use each skill via file system or tool-based bundles.

Discover how agents execute and access resources by loading skill metadata. Run scripts and fetch assets on demand, with sandboxing, allow listing, and logging.

See an open-source PowerPoint PPTX skill augmented inside an agent to create animated slides with morph transitions, using Python scripts to edit and read PowerPoint files.

Learn how skills work, decide which skills to create, and author them effectively to extend your AI agent's capabilities, with practical examples.

Author skill metadata with YAML front matter to boost LLM discoverability, using open source education and Anthropic standards. Emphasize keywords and descriptions that activate front end design skills.

Design context for LLMs using skill.md and markdown structure with bullets, bold, and headers. Read, update, and iteratively tweak your files with an LLM to guide inference in anthropic skills.

Understand domain expertise as a skill aligned to a business or technical domain, and see branding and widget skills improve LLM code readability and domain-driven prompts.

Extend AI agent capabilities by adding Python scripts as skills, enabling the LLM to edit Word documents, pdf files, PowerPoint presentations, and Excel files, execute code, and automate steps.

Design repeatable workflows with agent skills using structured stages, clear triggers, and iterative refinement to guide large language models reliably, illustrated by co-authoring documentation patterns.

Write skills once, use anywhere across Claude, Codex, Cursor, Copilot by following the interoperable agent skills standard; install via documentation and skill files, regardless of location.

Explore a growing repository of pre-existing skills, learn to use open-source skills from skills.sh and GitHub, and share trusted skills with your team.

Install the skill creator and set up your environment to build your own ai skill. Test it in an llm using tools like opencode, cloud code, or gemini cli.

Explore why agent skills exist, their anatomy, and how AI agents use them under the hood; learn to author good skills and how to find and use them.