To prevent context overload as your foundational layer grows, each file should include a header that tells an AI skill when to use it. The skill then scans and loads only the relevant files for a given task. This ensures the AI has the right context without getting confused by irrelevant information.

Avoid creating a single, massive context document that quickly becomes stale. Instead, maintain 3-5 small, focused, and dated files on specific topics (e.g., team, product). Treat context as an ongoing practice of curation: whenever you re-explain something to the AI, it should be added to a context file.

Counterintuitively, the goal of Claude's `.clodmd` files is not to load maximum data, but to create lean indexes. This guides the AI agent to load only the most relevant context for a query, preserving its limited "thinking room" and preventing overload.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.

The easiest way to teach Claude Code is to instruct it: "Don't make this mistake again; add this to `claude.md`." Since this file is always included in the prompt context, it acts as a permanent, evolving set of instructions and guardrails for the AI.

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.

To build a robust personal OS in Claude Code, replicate OpenClaw's architecture. Use a master instruction file (e.g., `claude.md`) that systematically imports context from separate files for identity, user info, and a comprehensive tools list (`tools.md`).

Instead of overloading the context window, encapsulate deep domain knowledge into "skill" files. Claude Code can then intelligently pull in this information "just-in-time" when it needs to perform a specific task, like following a complex architectural pattern.