research-field-onboarding

Language: English | 中文

A skill that makes an AI assistant ask what you already know before it explains an unfamiliar research field, teach one rung at a time, and label every paper it cites as verified or unverified.

Quick navigation

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.

The problem is not that the model knows too little. It is that nobody asked you what you know before choosing where to start.

This skill is not a knowledge base, and it does not carry a library of field summaries. A field-specific corpus goes stale the moment it is written, and shipping five fields well would silently degrade every other field. What it carries instead is a discipline:

locate the reader first, then teach upward from there, one step at a time.

The model already knows the physics. What it lacks, by default, is the habit of asking where you are standing before it starts pointing.

Who this is for

This is built around a specific situation: you are competent somewhere, and you need to get oriented somewhere else.

• Graduate students and postdocs reading outside their specialty, where the hard part is not intelligence but knowing which of the unfamiliar terms you are allowed to skip.
• Researchers evaluating a method from a neighbouring field, who need to decide whether it fits their problem rather than to master it.
• Anyone who has been burned by a fabricated citation, and wants a reading path where every item is labelled.
• Reviewers, examiners, and supervisors who have to parse a manuscript outside their area.

It is deliberately less useful if you are a specialist asking inside your own field, or if you want a one-line factual answer. In both cases it is designed to get out of the way. See Scope.

Quick start

Claude Code — one command:

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

Or copy field-onboarding/ into ~/.claude/skills/. Drop -g, or use .claude/skills/, to scope it to a single project instead.

Claude apps (claude.ai, Desktop, Cowork) — desktop and cloud sessions do not read ~/.claude/skills/. Enable the skill on your account instead, via Customize in the Desktop sidebar or the skill settings on claude.ai.

ChatGPT Skills — use field-onboarding/ as the skill root, keep SKILL.md at that root and agents/openai.yaml alongside it, and package it with the skill packaging tool in your environment.

Codex or another repository-aware agent — clone this repository into the workspace and keep AGENTS.md at the root. It tells the agent when to load the workflow.

Anything else — hand the agent field-onboarding/SKILL.md as its instruction file.

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.

How to invoke it

Triggering is governed by the description in field-onboarding/SKILL.md, not by a magic word. Anything that signals I do not know this field yet should work. The reliable patterns:

• Name the field and ask to be taken through it. Guide me into <field> step by step. I'm new to <field> — walk me through it.
• State your bridge. I know <what you have> but not <field>. This is the strongest form: it hands the agent an anchor before it has to ask.
• Hand it text. Paste an abstract, a figure caption, a referee comment, and ask what it is saying.
• Ask for a path. Give me a reading path into <field>.
• Say the last answer failed. That was too technical — start further back.
• Say what it is for. I need to build a <setup>, teach me what I need. The stated target changes the shape and length of every rung, so saying it up front is worth a sentence.

Chinese works the same: 一步一步带我入门<领域>、这篇摘要看不懂、给我一条<领域>的阅读路径。

What it does differently

1. It names your gaps for you. Not "what's your background?" — you cannot audit a gap you cannot see. The agent works out the three to five upstream frameworks the topic actually rests on, lists them with a one-clause gloss, and asks you to mark each as used it, learned it, or new. Then it uses the marks: anchors are never re-taught, black boxes are declared as black boxes, and a load-bearing gap gets built before anything stands on it.

2. It teaches one rung at a time. Motivation, vocabulary, core framework, methods, frontier. Each rung is one turn and ends with a real diagnostic — a prediction, a restatement, a forced choice — not "does that make sense?", which always gets a yes. Wrong answers get a different explanation, not a louder one.

3. It will not invent a reference. Reading paths are where models fabricate, and a plausible title with a plausible year costs you an afternoon. Every named work is either verified in-session with a DOI or arXiv ID, or explicitly labelled from memory, unverified. There is no third option, and an identifier that was not retrieved is never attached.

4. It knows when to stay out of the way. Ask a narrow factual question and you get a narrow factual answer. The ladder is offered once, in one line, and dropped if you do not take it.

Scope: when it fires, and when it does not

It should run when you are new to a field, cannot parse a paper or abstract, ask for a step-by-step walkthrough or a reading path, or say an explanation was too technical. It fires even if you only name an unfamiliar field without asking to be taught.

It should not run when the question is narrow and factual, when you are a specialist asking inside your own field, when you asked for it short, when the task is translation, editing, formatting, debugging or a search with a known target, when you are blocked mid-experiment and need the fix, or when you already declined the ladder earlier in the session.

The governing heuristic is the one-turn test: if a single turn answers the question well, the agent answers it and then offers the ladder once. Answer first, offer second.

The ladder

Your target routes the whole ladder, not just its length. Reading a paper weights notation and formalism; judging whether a method fits your work leads with phenomena and worked numbers and expands Rung 4; doing it hands-on turns Rung