I am experimenting with knowledge management, see https://www.skool.com/quantum-quill-lyceum-1116/validating-the-idea-of-modular-context-architecture?p=85f985fa During a linting session i noticed my skill was verbose and inefficient so i thought: "i wish i had a way to manage knowledge for skill....wait.lol, i do have it" So my question is: Did any of you tried to create a wrapper skill that route subskills when needed to prevent context bloating? is the same principle over and over but i never applied to skills.

I’m following the courses and making my first step to treat knowledge as code. ## Stack: Obsidian + NotebookLM + ContextMode + Docker + Git Thanks to mouthful Gemini i call this: Modular Context Architecture (MCA). In this system, each folder represents a specific "Knowledge Node" with a standardized file syntax to optimize how an agent routes and consumes information. ## The Node Structure Each node (folder) contains four primary files designed to tier information by depth of intent: 1. README.md (Entry Point): Contains the minimal viable information required to identify the node’s purpose. This serves as the first layer for an agent’s broad search or initial embedding match. 2. CONTEXT.md (The Schema): A deeper explanation of the node’s internal logic. It acts as a map, referencing more granular sub-files (e.g., Specific_Detail_A.md) that should only be loaded if the agent requires higher resolution on the topic. 3. REFERENCES.md (The Router): A dedicated file for internal [[wikilinks]] and external URLs. This separates the relational graph from the descriptive prose. 4. MEMORY.md (Heuristics): An optional file containing localized constraints, formatting rules, or "long-term" logic that the agent must apply strictly when working within that specific node. ## Example: Business Model Canvas - The Graph: The complete canvas. - The Node: The "Value Proposition" folder. - The Execution: An agent enters the "Value Prop" directory. It identifies the node via README.md. If the task requires analyzing customer pain points, the CONTEXT.md directs the agent to a specific Pains.md file. Throughout the process, the agent adheres to tone and formatting constraints defined in MEMORY.md. ## Technical Advantages - Context Efficiency: The agent only retrieves the specific "layer" of documentation necessary for the current task. - Scalability: New domain knowledge can be integrated by adding a standardized folder without restructuring the existing graph. - Human-AI Interoperability: The vault remains navigable for human users while providing a clear, API-like structure for LLM parsing.

You just used Claude Desktop as a thinking partner instead of a vending machine. Vote below, then tell us in the comments: what was the problem, and what was the insight you walked away with?

You just ran Claude Code on your own files. Vote below, then drop what you made in the comments. What was the task? What did you get back?

You just saw how the folder structure adapts to different use cases. Vote below, then drop your customized setup in the comments. What did you name your workspaces and why?