1. The Skill Graph
Six reference universities, each selecting for its own independent group of the skills (Stanford · CMU · Tsinghua · Cambridge · Oxford · Berkeley) — so the radars have different axes, not one shared shape. Toggle two or more to overlay them on one graph for comparison (a skill not in a style sits at the center). Team leads and board mark the styles they ❤ love.
2. The Skills, Explained — In Detail
graphed = scored by test problems, becomes a radar axis. qualitative = assessed from documents and interview, never scored. A skill is approved when more than 2 of the 4 members accept it (3 or 4 of: 3 team leads + board) — then it moves to the test list.
Intellectual Vitality umbrella
Being deeply curious and loving learning because you genuinely enjoy understanding things — not just to get grades, win awards, or make money. Stanford rates this separately because it wants students who will create new knowledge, not just consume it. It is the umbrella over the four skills below: curiosity, joy in learning, persistence, and independent learning.
Strong signals- Reading science articles or books far beyond the school program — for fun, not for credit.
- Building your own small app, game, or gadget just to understand how it works inside.
- Spending a weekend on a question nobody assigned — "how does a search engine decide what comes first?"
- Asking deep questions and exploring beyond what is required.
Genuine Curiosity graphed
A natural desire to understand how things work — refusing to accept facts without asking why.
Strong signals- Instead of just accepting "AI can recognize faces," you keep asking: How does it actually do that? Why does it need thousands of example photos to learn? When does it make mistakes? Could it be fooled — and how would we test that?
Joy in Learning for Its Own Sake qualitative
Learning because learning itself is rewarding.
Strong signals- Learning a new programming language, instrument, or foreign language nobody asked you to learn.
- Watching a documentary about how the internet works simply because you find it interesting.
- "I only learn this because it will be on the exam."
Persistence / Resilience graphed
Continuing despite setbacks and difficulties. In the famous West Point study, grit predicted completing elite training better than the official admission score — and CMU's review committees name "persistence and resilience in the face of challenge and adversity" as an explicitly valued quality.
Strong signals- Spending weeks on a bug in your project that everyone else gave up on — and fixing it.
- Entering the competition again after losing, with a better plan each time.
- Continuing a long project after many failed attempts — changing the approach instead of quitting.
Independent Learning / Teachability graphed
How fast you absorb a new concept and correctly apply it — the criterion elite universities probe hardest in interviews, and the #1 trait in hired-without-a-degree stories at top technology companies.
Strong signals- A test problem teaches you an unfamiliar notation, and two lines later you use it correctly.
- Given a hint in the interview, you absorb it and immediately build on it.
Mathematical Reasoning graphed
Rigorous step-by-step analytical thinking — ranked the #1 skill by employers worldwide and the foundation of every machine-learning course.
Strong signals- Proving a pattern instead of stating it (e.g., why perfect-square lockers stay open).
- Translating a formula into a working procedure.
Creativity / Risk-Taking graphed
Finding the elegant, non-obvious path — and being willing to try an approach that might fail. CMU looks for it as independent problem-solving shown in real projects; Tsinghua calls it innovative thinking.
Strong signals- Spotting the shortcut where everyone else grinds through the slow standard path.
- Proposing an unusual design in an open-ended problem and defending its trade-offs.
Communication & Persuasion graphed
Explaining your reasoning so a teammate can follow it — and arguing a position convincingly. In a competitive research world, an unexplained result is an unused result.
Strong signals- A pseudocode answer whose logic a non-author can re-implement.
- A persuasive professional email that proposes a solution, not just an excuse.
Collaboration & Initiative graphed
Working well with others — and starting things without waiting to be told. CMU's review committees explicitly value leadership and concern for community alongside technical depth.
Strong signals- Building on a teammate's idea instead of competing with it; bringing quiet members into the discussion.
- Volunteering structure ("let's list the options first") when a group is stuck.
Integrity qualitative
Being honest, ethical, and trustworthy.
Strong signals- Reporting experimental results accurately, even when they contradict your hypothesis.
- Not taking credit for others' work; following research ethics.
- Saying "yes, I've seen this problem before" when offered a fresh one in the interview.
Depth of Commitment qualitative
Sustained dedication to one meaningful interest over years. Committees prefer one deep passion over many disconnected activities, because depth predicts significant contribution.
Strong example- 4 years deepening one passion: simple games → a website for the school club → the database behind it → a small published app — every step deeper in the same direction.
- Robotics 2 months · blockchain 1 month · stock trading 3 weeks · biology 2 months — 20 clubs, 10 hackathons, 15 online courses, but no substantial project or long-term mastery.
Leadership graphed research
Influencing and inspiring peers, starting projects, and giving a group direction — distinct from simply collaborating. Named explicitly by CMU (a core pillar: "produce the next generation's leaders"), MIT, Berkeley, and Tsinghua.
Strong signals- Founded or ran a club, team, or project — moved others toward a goal.
- In the group stage, raises the whole team's output, not just their own.
Attention to Detail / Rigor graphed research
Precision and correctness — checking edge cases, testing assumptions, and intellectual honesty under scrutiny. Oxford and Cambridge select for mathematical rigor; DeepMind and Anthropic name rigor and "intellectual honesty under scrutiny" as what distinguishes strong researchers.
Strong signals- Spots the empty-list / off-by-one / overflow case before being told.
- Reports a result honestly even when it contradicts their hypothesis.
Ownership / Accountability graphed research
Sees work through end-to-end and cares whether the whole thing ships — not just "my part works." Remote-first companies hire "managers of one"; CMU values students who initiate with a sense of purpose.
Strong signals- When blocked, finds a way forward instead of waiting to be told.
- Follows a task to completion and keeps stakeholders informed.
Systems Thinking graphed research
Reasoning about whole systems and how their parts interact — trade-offs, data flow, failure modes — not just a single function. DeepMind explicitly names "systems-level thinking" as a core researcher quality.
Strong signals- Asks where the data comes from and what could go wrong across the whole pipeline.
- Weighs trade-offs (speed vs. memory vs. accuracy) rather than optimizing one part blindly.
3. Career Target Profiles — What Are We Selecting For?
Dashed purple = the ideal shape each career demands; solid = closest reference profile. The gap between them is what mentoring must close.
4. How We Pick the Top 10
The rule we apply, in order:
- 1. Rank by total score — written (100) + verbal (10) + group (10).
- 2. Borderline seats: prefer balanced shapes. Holes in persistence or independent learning are the riskiest for the work-while-study format.
- 3. Cohort coverage — the 10 admits should span all three career targets.
- 4. Qualitative flags — decided separately and documented, never merged into the score.
5. Summary — Total Skill Status
All scores, votes, and decisions in one live table. Green = strongest axis, red = weakest.
6. Team Lead Requirements — Editable
Each team lead edits their team's required shape: slide levels · “del?” marks a shared skill for deletion (kept in the database, removed later at board review) · quick add an existing skill (☆) · or define a new skill (★) in full card format. Saves automatically.
7. Our Own Skill Framework
The final selected skill set — every skill the committee approved (3+ of 4) plus every skill the team leads added (auto-approved), in one list, with how strongly each is tested. This is the framework we build our selection on.