To maximize AI's impact, treat LLM skills and prompts like a centralized codebase. When one person discovers a better technique, it should be integrated into a shared, version-controlled repository, ensuring the entire team benefits from individual learnings.

Manage collective team context—docs, queries, research—in a version-controlled repository. Everyone, including non-technical members like ops and strategy, contributes via pull requests, creating a single, evolving source of truth for AI agents and humans.

Rather than passively waiting for model improvements, 'skill engineering' is emerging as a discipline. It involves actively encoding expert workflows, quality gates, and even subjective 'taste' into portable components for AI agents, allowing organizations to consistently improve agent performance on specific tasks.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

OpenAI structures its repositories to be a complete, self-contained knowledge base for AI agents. All project artifacts—design docs, historical implementation plans, and even text versions of external library documentation—are checked in, allowing the agent to find any needed context via simple search.

Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

"Skills" are markdown files that provide an AI agent with an expert-level instruction manual for a specific task. By encoding best practices, do's/don'ts, and references into a skill, you create a persistent, reusable asset that elevates the AI's performance almost instantly.

Centralized AI skill libraries are more than automation tools; they are the modern realization of knowledge management. They codify best practices and organizational knowledge into portable, executable artifacts for both new employees and AI agents to use.