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.

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.

Agentic frameworks like OpenClaw are pioneering a new software paradigm where 'skills' act as lightweight replacements for entire applications. These skills are essentially instruction manuals or recipes in simple markdown files, combining natural language prompts with calls to deterministic code ('tools'), condensing complex functionality into a tiny, efficient format.

A high-performing AI marketing system uses specific context files (e.g., copy.md, audit.md) for each skill, rather than a single brand guide. This provides the AI agent with tailored instructions and best practices for the specific task at hand, dramatically improving output quality.

The "Agent Skills" format was created by Anthropic to solve a key performance bottleneck. As capabilities were added, system prompts became too large, degrading speed and reliability. Skills use "progressive disclosure," loading only relevant information as needed, which preserves the context window for the task at hand.

"Skills" in Claude Code are more than saved prompts; they are named functions packaging a prompt, specific execution heuristics, and a defined set of tools (via MCP). This lets users reliably trigger complex, multi-step agentic workflows like deep chart analysis with a single, simple command.

Reusable instruction files (like skill.md) that teach an AI a specific task are not proprietary to one platform. These "skills" can be created in one system (e.g., Claude) and used in another (e.g., Manus), making them a crucial, portable asset for leveraging AI across different models.

Instead of loading large context files on every turn, use "skills." The agent only sees a skill's name and description initially, loading the full instructions only when needed. This method, called progressive disclosure, drastically saves tokens and improves performance.

Tasklet completely re-architected its agent, moving from feeding chat history into the LLM to treating the file system as the primary context. The agent now receives hints and pointers to relevant files, enabling it to handle infinitely long histories and larger contexts beyond the token window.