I looked at 11 recent methods for giving AI the right knowledge and context. ICM, Agent Skills, OKF, LLM Wiki, Second Brain and more.
They differ a lot. But they share three things:
- Plain files and folders hold the knowledge.
- Context comes in layers: a short summary first, detail only when needed.
- The structure itself tells the model what to do and what to read.
The differences are in the slides. The main similarity is clear: Hierarchy.
Out of all of them:
ICM constraints you the most. It limits you by forcing you to split your work hierarchically. And I would bet that is exactly the reason why it sits far above all the other methodologies. It has clear standards and design patterns that one could follow and improve upon.