The 10 skills real AI-engineering intern roles (like Clinikally YC S22's) actually ask for. Drag to orbit · tap a star to inspect · mark what you know.
Each skill carries a weight reflecting how often it appears in real AI-engineering intern postings (agents 15%, LLM APIs 13%, Python 12%, RAG 12%, evals 11%, multi-agent 9%, prompting 8%, deployment 8%, vector DBs 7%, Git/SWE 5%). Your score is simply the sum of the weights you have marked:
readiness = Σ weight(skill) for every skill you know
The "learn next" suggestion is a greedy pick: the highest-weight skill you don't have yet whose prerequisite edges in the constellation are all satisfied. That mirrors how learning actually compounds — RAG before you know embeddings is memorizing recipes; agents before tool-calling is cargo-culting frameworks.
When a YC startup like Clinikally (S22) writes "build AI agents and multi-agent systems, LLM applications," the day-to-day work is rarely training models. It is: wiring frontier-model APIs into product features, designing tool schemas and prompts, grounding answers in company data with RAG, and — the part most candidates skip — writing evals so quality is measurable. Hiring managers consistently rank a shipped public project (an agent with traces and an eval suite) above coursework.
1) Depth beats breadth: 70% on this tree with one polished project outperforms 100% checkbox knowledge. 2) Read the model provider docs, not just framework docs — frameworks churn, APIs endure. 3) Cost awareness is a skill: interns who can say "this agent run costs $0.04 and here's why" stand out immediately.