Research Field Onboarding

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Ask an assistant about a field you don't know and it answers at the level of someone who does. This one checks the field's own structure, asks which of its prerequisites you already have, teaches one step per turn, and never names a paper it hasn't checked.

🆕 New in v1.9 — fewer questions up front, and one part of the field per turn. The opening turn asks only which prerequisites you have and what you want, in one go, and takes those prerequisites from the field's own textbook or review. After the motivation and vocabulary, each part of the field gets a turn of its own, and a part you got wrong stays marked shaky until you get it right. Say "derivation first" or "show the map" whenever you like. Jump to the ladder.

If it helps you, a ⭐ makes it easier for other people to find. Watch the repo if you want to hear when the rules change; the changelog has moved most weeks, mostly because I keep using it on my own reading and finding places where it breaks.

Quick start

Paste this into your agent, whichever one you use:

Install the field-onboarding skill from
https://github.com/ljx-chase/research-field-onboarding, following the
installation section of the repo's AGENTS.md.

It works out where its own skills directory is. If you would rather do it by hand, AGENTS.md has the per-agent paths, and on Claude Code there is a one-liner:

npx skills add ljx-chase/research-field-onboarding -g

Claude web and ChatGPT want a zip; there is one in releases.

That is the whole setup. Users never run the bundled state script or edit its JSON; capable agents use it internally, and other agents fall back to the same prompt workflow.

Then try:

I understand nonlinear optics but not topological photonics. Guide me into the field step by step.

A good first response names the prerequisites and asks which ones you already have. It does not open with a paragraph of definitions.

Why this exists

Ask an assistant about a field you do not know, and it will answer at the level of someone who already does. The answer is correct. You cannot use it, and you cannot tell which of its twelve terms you were supposed to already know. Ask again and you get more terms.

Without it

You: Explain attention in transformers.

Assistant: Transformers replace recurrence with self-attention, projecting each token into query, key and value vectors, scoring them by dot product, normalizing with softmax, then concatenating multiple heads through a residual connection into a feed-forward block with layer norm and positional encoding.

You: Which of those am I supposed to already know?

Assistant: Self-attention maps each token to three learned projections, then takes a similarity-weighted sum over the sequence.

With it

You: Explain attention in transformers.

Assistant: Before we start: the shortest bridge from what you already know. Mark each one.

• Matrix multiplication and dot products — attention is a stack of matrix operations
• Softmax and probability normalization — this is where the attention weights come from
• Embeddings and sequence representation — how text becomes vectors the model can work on

used it / learned it / new