superintelligence-ladder
Skill0 starsChanged 6 months agoMITView on GitHub
Six-level maturity ladder for AI use in admin work, from "asking" to "native organization." Self-assessment per level. Concrete next move per level. Use when: assessing where you are, planning a roadmap, onboarding a colleague, briefing a manager on AI strategy, deciding what to install next.
In this repository10 other files it gives its agents.
- brand-voiceSkill
- animated-slideshowSkill
- enterprise-board-panelSkill
- enterprise-department-routerSkill
- quality-gateSkill
- commentary-engineSkill
- morning-briefSkill
- sensitivity-templatesSkill
- prompt-power-wordsSkill
- sparring-partnerSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Read each level. Find yourself honestly.
- Distinctive: line 44
Your next move: Do the Memory Bootstrap. Tell Claude who you are, how you work, what you do not want it to do.
- Distinctive: line 111
Self-assessment: Read each level honestly. The point is not to climb fast, it is to climb soundly.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
The Superintelligence Ladder
Six rungs. You climb one at a time. Each rung is a small step from the one below. Most admin people in 2026 sit at Level 1 or 2. The highest rung is rare but reachable, and it is changing what one person can do at work.
Read each level. Find yourself honestly. Read your next move. Take it this week.
Level 1 · Asking
You use Claude or ChatGPT as a search engine you can talk to. One question, one answer. Nothing remembers anything. Nothing connects to anything.
You are here if:
- You have used a chat AI a few times for personal tasks
- You have not used AI for a real work deliverable yet
- You assume AI is a fancier Google
Typical gain: zero to ten percent on routine tasks. Output quality is variable and often disappointing because the AI has no idea who you are or what good looks like for you.
Your next move: Pick one task you do every week. Try Claude on it once. Compare the output to your usual. Save the prompt if it helped.
Level 2 · Drafting
You use Claude in your daily work. You have three to five saved prompts. You paste in data, you get drafts back. Output is rough but useful. You correct it before sending.
You are here if:
- You use AI three or more times a week for real work
- You have favorite prompts you reuse
- You still feel like the AI is guessing at what you actually want
Typical gain: twenty to forty percent on writing tasks. Quality is improving but inconsistent.
Your next move: Do the Memory Bootstrap. Tell Claude who you are, how you work, what you do not want it to do. Outputs change overnight.
Level 3 · Personalized
Claude has memory of who you are, how you work, and what matters. Outputs sound like you would have written them. You have connected one or two tools (Outlook, Teams, SharePoint) so Claude can read live context. Most days, the first draft is publishable with light edits.
You are here if:
- Memory is set up and you trust it
- At least one MCP connector is in place, read-only
- Your colleagues notice the writing feels like yours, not like AI
Typical gain: fifty to seventy percent on routine work. Quality matches your average effort, every time.
Your next move: Install one plugin for your function. Pick the skills that match the tasks you do most. Let them auto-trigger. Stop typing out the same instructions.
Level 4 · Equipped
You have plugins installed for your function. Skills auto-trigger on the right tasks. A quality gate catches obvious slop before it leaves your desk. When a hard decision lands, you ask a panel of perspectives before you commit. Output consistently matches your best work, not your average work.
You are here if:
- Three or more skills auto-trigger correctly on different tasks
- You have a quality gate that has actually caught something
- You have used a panel pattern at least once for a real decision
Typical gain: sixty to eighty percent on routine work. Quality at your best, every time. Hard decisions feel less alone.
Your next move: Stand up your first scheduled task. Pick the one piece of information you wish appeared on your screen before you sit down. Morning brief is the usual entry point.
Level 5 · Operating
Scheduled tasks run while you sleep. Morning brief, weekly market scan, regulatory radar, monthly retro. Self-evolving loops adjust your prompts based on what works. Memory grows automatically each week. A board-style panel rehearses your hard decisions before the real meeting. The system works when you are not working.
You are here if:
- Three or more scheduled tasks run reliably without your attention
- A monthly retro has actually changed how you work
- You have caught yourself thinking "the system handled that for me"
Typical gain: seventy to ninety percent on routine work. Plus you have capabilities you did not have before (deep market scanning, board rehearsal, regulatory tracking) that did not exist for a one-person operation.
Your next move: Find one colleague who is at Level 2. Hand them this ladder and your Setup Zero. Get them to Level 3 this month. The transition begins.
Level 6 · Autonomous
The system acts on goals, not on tasks. Given an outcome, it plans, executes, and only escalates the judgment calls. Subagents collaborate across days. Memory is shared across the function. The AI is no longer waiting for prompts. It is the operating layer underneath the work.
You are here if:
- Goal-level interfaces are normal ("achieve X by Y date") rather than task-level
- Subagents coordinate without you orchestrating each step
- Escalation rules are documented and the system honors them
- New work types get added to the operating layer, not just to a todo list
Typical gain: measurement shifts. Hours saved is no longer the meaningful unit. The unit is now goals achieved per person per month, errors caught before they shipped, decisions made better because the prep work was done before the meeting started.
Your next move: there is no Level 7 today. The frontier beyond Level 6 will be drawn when this rung is climbed.
How to use this ladder
- Self-assessment. Read each level honestly. The point is not to climb fast, it is to climb soundly.
- Skip nothing. Each rung makes the next one possible. People who try to skip from Level 2 to Level 5 fall back to Level 1.
- Bring colleagues with you. Level 5 alone is impressive. Level 5 across a team is transformative.
- Re-check every quarter. You will move. The ladder will move. The rungs may need to be redrawn next year.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

