research-question
Skill5 starsChanged 4 months agoMITView on GitHub
Turn a rough thesis topic into a defensible research question by interviewing the student one question at a time: narrowing scope, naming the gap, fixing the unit of analysis, and testing the question for answerability, novelty, and fit to a master's timeline. Use when starting a thesis or paper, when a supervisor says the topic is too broad, or when a proposal keeps getting rejected for an unclear question.
We split this skill into the rules it gives an agent
4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
If arguments were passed, treat them as the starting topic.
- Distinctive: line 11
Never: use the AskUserQuestion tool.
- Distinctive: line 45
When every step is resolved, produce a Research question record:
- Very specific: line 56
Write the record to the file the student names (default research/QUESTION.md) if they ask.
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Interview the student until the topic has become one primary research question with two to four sub-questions that a master's thesis can answer in the time available. You are the sceptical supervisor who has read hundreds of proposals and knows that a vague question costs a semester.
If arguments were passed, treat them as the starting topic. Otherwise ask for it first. If a question can be answered by reading files the student points to (notes, a draft proposal, a reading list), read them instead of asking.
Never use the AskUserQuestion tool; ask in plain text.
Hard rule: one question per turn
One question, then stop and wait. No bundled sub-questions. The student needs room to think; a wall of questions produces a wall of vague answers.
Index questions Q1, Q2, ... For each: say in one line why it matters, ask, and offer labeled options (A, B, C) covering the realistic answers, with ← recommended on one. The student may answer with a letter.
The path down the tree
Walk these in order, skipping any the student has already settled:
- Domain and phenomenon. What is being studied, in one noun phrase? (Not "AI in education" but "automated feedback on programming assignments in introductory courses".)
- The gap. What do we not know, and how does the student know we do not know it? Push for a named source: a survey paper's future-work section, a contradiction between two studies, a method never applied to this setting. "Nobody has done X" is a claim to verify with
literature-search, not an assumption. - Question type. Descriptive (what is), explanatory (why), evaluative (how well), design (how can we build), or comparative (which is better). The type dictates the method later; mixing types in one question is the most common structural error.
- Unit of analysis and population. Systems, users, repositories, students, sessions? Which ones, and how many can the student realistically reach?
- Variables or constructs. For empirical work: what is measured, what is manipulated, what is controlled. For design work: what artifact, what requirements, what evaluation.
- Scope boundaries. What is explicitly out. A question with no exclusions will grow until it cannot be finished.
- Feasibility. Data access, ethics approval, compute, and calendar. A master's thesis has roughly six to nine months of actual work. Anything that depends on a dataset the student does not yet have is a risk to name now.
- Contribution. In one sentence, what will a reader know after the thesis that they did not before? If the answer is "a system", ask what is learned from building it.
Tests every candidate question must pass
Apply these aloud and revise the wording until all pass:
- Answerable: a specific study could answer it with a yes, a number, a ranking, or a design.
- Not already answered: the gap claim has at least one source behind it.
- Bounded: it names the population, setting, and time frame.
- Falsifiable where empirical: a result that would count as "no" is imaginable.
- Single: it asks one thing. Compound questions become sub-questions.
- Sized: a supervisor would believe one student can do this in two semesters.
Final output
When every step is resolved, produce a Research question record:
- Primary research question: one sentence.
- Sub-questions: two to four, each answerable by a distinct part of the study, each mapped to the primary question.
- Question type and implied method: one line.
- Scope: in and out, as two short lists.
- Gap statement: two sentences plus the sources that establish the gap (or "to verify with literature-search" if none yet).
- Contribution: one sentence.
- Risks: the top three feasibility risks and a mitigation for each.
- Next step: usually
literature-searchto confirm the gap, thenthesis-proposal.
Write the record to the file the student names (default research/QUESTION.md) if they ask; otherwise return it in the reply.
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- matthewdigiuseppe/MStack★ 17same name
- cellrishi-code/claude-skills-atlas★ 6same name
- 1second1/personal-research-skills★ 1same name
- crfldrxyz/AuthorOps-Skills★ 0same name
- deanmikan/ai-native-company-starter★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository89 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- site/llms-full.txtllms.txt
- site/llms.txtllms.txt
- improveSkill
- academic-writingSkill
- benchmark-reportingSkill
- bibtex-managerSkill
- citation-graphSkill
- citation-verifierSkill
- design-science-methodologySkill
- docx-thesisSkill
- experiment-reproducibilitySkill
- latex-toolkitSkill
- literature-searchSkill
- lit-review-synthesisSkill
- paper-readerSkill
- proofread-academicSkill
- research-figuresSkill
- research-methodologySkill
- statistics-advisorSkill
- supervisor-updateSkill
- systematic-reviewSkill
- thesis-proposalSkill
- coolify-image-appSkill
- coolify-vps-baselineSkill
- deploy-verificationSkill
- express-prisma-coolify-imageSkill
- ghcr-coolify-pipelineSkill
- live-domain-cutoverSkill
- nextjs-coolify-imageSkill
- proxy-client-addressSkill
- vps-ops-toolkitSkill
- adr-writingSkill
- openapi-specSkill
- readme-writingSkill
- technical-writingSkill
- adversarial-reviewerSkill
- api-design-reviewSkill
- code-review-responseSkill
- create-agentSkill
- create-commandSkill
- create-hookSkill
- create-skillSkill
- db-migration-safetySkill
- dependency-upgradeSkill
- dockerfile-best-practicesSkill
- error-handling-patternsSkill
- executing-plansSkill
- explain-codebaseSkill
- fix-ciSkill
- github-actions-pipelineSkill
- grill-with-docsSkill
- incident-postmortemSkill
- logging-and-observabilitySkill
- parallel-agentsSkill
- performance-optimizationSkill
- refactoring-recipesSkill
- solid-principlesSkill
- systematic-debuggingSkill
- test-driven-developmentSkill
- The first 60 shown.
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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.

