Dimension 1 of 6
Context Architecture
How you structure your information environment — connections, documents, persistent contexts, and organizational knowledge — so AI can reason over your work without you re-explaining it every session.
The five levels
Absent
Starts fresh each session. Copies and pastes context manually. No data connections. Hasn't yet thought of their work as decomposable into AI-legible steps. This is where most knowledge workers outside Tribe begin — the tools are new and the mental model of "structuring context for AI" doesn't exist yet.
Personal
Has Claude Projects or saved prompts. A few data connections. Can describe their workflow but hasn't externalized it for AI. Uses voice for context input. Session-to-session context is manual but intentional.
Systematic
Several data connections feeding real work data. Persistent contexts across sessions (CLAUDE.md files, project structures). Has decomposed their workflow into AI-delegable and human-judgment steps. Skills that encode recurring patterns.
Expert Exemplar
Many data connections orchestrated into coherent information flows. Designs context delivery systems, not just individual connections. Restructures documents and codebases for AI legibility. Skills and plugins that compose into larger workflows. Others adopt their context patterns.
Compounding
Designs and builds context systems that other people plug into — layered context architecture (org-wide defaults → project-specific → personal) that others adopt and extend without your involvement. Actively restructures shared knowledge (codebases, documentation, organizational knowledge bases) for agent legibility, not just personal legibility. Creates context templates and patterns that new team members inherit on day one. Your context architecture decisions become the default way others set up their environments.
Key quotes
“I haven't been here long enough to understand what my workflow is yet completely.”
“I understand the scoping process, but I don't necessarily write it out explicitly or identify it... chaos workflow. It's like, oh, crap, I need to do this.”
“I was getting annoyed about telling it to do a sequence of things. So I just turned that into a slash command where I was like, I want you to commit, push and deploy on Railway with these settings.”
“The model is, like, a really good employee on day one... Go read this doc and go read that doc, and then read the code and wrap your head around it. Take whatever time you need to understand it. Then let's chat.”
Transitions — what distinguishes each level
The shift is from *no awareness* to *awareness without infrastructure*. At L2, you can describe your workflow and you've started saving context (Projects, prompts), but you haven't built systems that deliver context to AI automatically. The gap is between "I re-explain everything each session" and "I have some saved starting points."
The shift is from *manual, session-based context* to *persistent, automated context delivery*. At L3, your MCPs feed real data, your CLAUDE.md files persist across sessions, and you've decomposed your workflow into AI-delegable steps. The gap is between "I can describe my work to AI" and "my work environment is structured so AI already knows."
The shift is from *effective personal setup* to *designed information architecture others adopt*. At L4, you're not just connecting tools — you're designing how information flows between them, restructuring documents for AI legibility, and creating patterns others copy. The gap is between "my setup works for me" and "my setup becomes the reference others learn from."
The shift is from *personal excellence others imitate* to *organizational infrastructure others inherit*. At L5, new team members plug into your context architecture on day one without needing to learn your approach through observation. The gap is between "people copy what I do" and "people inherit systems I built."