Creating "skills" (e.g., Markdown files) to teach AI agents how to interact with a codebase forces developers to explicitly document processes and best practices. This AI-centric documentation serves a dual purpose as a clear contribution guide for humans, effectively turning what should be a `contributing.md` file into a machine-readable, actionable standard.

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.

Instead of using siloed note-taking apps, structure all your knowledge—code, writing, proposals, notes—into a single GitHub monorepo. This creates a unified, context-rich environment that any AI coding assistant can access. This approach avoids vendor lock-in and provides the AI with a comprehensive "second brain" to work from.

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.

To keep your AI agent efficient, differentiate between global and project-level skills and context files. General-purpose tools, like a text truncation skill, should be global. Specific processes, like a referral template, should be kept at the project level to avoid cluttering every interaction.

A more effective way to increase developer velocity with AI is to have champion engineers embed knowledge directly into the systems. This includes creating context engineering techniques, `agents.md` files, and agent skills within the repo itself. This way, any agent pointed at the repo benefits, rather than relying on every individual developer's expertise.

By storing AI skills in a company-owned GitHub organization, you treat them as valuable, proprietary assets. This ensures the company retains ownership and control over these automated processes, even if the employees who created them depart, protecting crucial operational knowledge.

Current AI skill development is single-player. Like early word processing documents, skills live on individual machines, creating versioning chaos and preventing teams from building a shared knowledge base. This "Microsoft Word era" of skills hinders collaborative improvement and scalability.

As teams adopt AI, individuals create disparate workflows, leading to inconsistency. Solve this by building an organizational skills library. Vetted, high-performing AI workflows are shared, ensuring everyone uses the best-in-class process for common tasks.