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From science-superpowers
Guides framing fuzzy research questions into precise, falsifiable investigations before any data is loaded or analyzed. Enforces a hard gate to prevent confirmatory contamination.
npx claudepluginhub k-dense-ai/science-superpowers --plugin science-superpowersHow this skill is triggered — by the user, by Claude, or both
Slash command
/science-superpowers:framing-research-questionsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Help turn a fuzzy research interest into a precise, falsifiable question with explicit hypotheses, the data required, and what would count as an answer — through natural collaborative dialogue.
Creates concrete analysis plans from approved research questions, covering model specs, confounds, power, and pipeline structure. Use before touching outcome data or fitting models.
Socratic guide for designing a research study through sharpening questions, mechanism, identifiability, validation, and risk. Use after topic selection to produce a `.research/design_brief.md`.
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Help turn a fuzzy research interest into a precise, falsifiable question with explicit hypotheses, the data required, and what would count as an answer — through natural collaborative dialogue.
Start by understanding the context (the data on hand, the domain, what your human partner already knows), then ask questions one at a time to sharpen the interest. Once you understand what is being investigated, present the framing and get approval.
Do NOT load the dataset, compute any statistic, fit any model, plot any outcome, or invoke any execution skill until you have presented a research framing and your human partner has approved it. This applies to EVERY investigation regardless of perceived simplicity.Why this gate is strict for science specifically: Looking at outcomes before the question and predictions are fixed contaminates a confirmatory analysis. Once you have seen the data, you cannot un-see it, and every later choice (which test, which subgroup, which cutoff) becomes suspect. Framing first is what keeps a result confirmatory rather than a story told after the fact.
Every investigation goes through this process. A single t-test, a quick correlation, a "just look at the trend" — all of them. "Simple" questions are where unexamined assumptions and undeclared researcher degrees of freedom do the most damage. The framing can be short (a few sentences for a truly simple question), but you MUST present it and get approval.
You MUST create a task for each of these items and complete them in order:
docs/science-superpowers/questions/YYYY-MM-DD-<topic>.md and commitdigraph framing {
"Explore context" [shape=box];
"Ask clarifying questions" [shape=box];
"Propose 2-3 framings" [shape=box];
"Present framing sections" [shape=box];
"Partner approves framing?" [shape=diamond];
"Write question doc" [shape=box];
"Self-review (fix inline)" [shape=box];
"Partner reviews doc?" [shape=diamond];
"Invoke surveying-prior-work" [shape=doublecircle];
"Explore context" -> "Ask clarifying questions";
"Ask clarifying questions" -> "Propose 2-3 framings";
"Propose 2-3 framings" -> "Present framing sections";
"Present framing sections" -> "Partner approves framing?";
"Partner approves framing?" -> "Present framing sections" [label="no, revise"];
"Partner approves framing?" -> "Write question doc" [label="yes"];
"Write question doc" -> "Self-review (fix inline)";
"Self-review (fix inline)" -> "Partner reviews doc?";
"Partner reviews doc?" -> "Write question doc" [label="changes requested"];
"Partner reviews doc?" -> "Invoke surveying-prior-work" [label="approved"];
}
The terminal state is invoking surveying-prior-work (then designing-the-analysis). Do NOT jump to loading data or fitting models. The ONLY skills you invoke after framing are surveying-prior-work and designing-the-analysis.
Understanding the interest:
A good research question is:
Exploring framings:
Presenting the framing:
Documentation — write the approved framing to docs/science-superpowers/questions/YYYY-MM-DD-<topic>.md:
# <Question title>
**Research question:** <one precise, falsifiable sentence>
**Background / motivation:** <why this matters, what decision it informs>
**Hypotheses:**
- H0 (null): <...>
- H1 (alternative, directional if justified): <...>
**Population & unit of analysis:** <who/what, the sample, the unit>
**Key variables (operationalized):**
- Outcome: <construct> → <measure / column / computation>
- Predictor(s) / exposure: <...> → <...>
- Covariates / potential confounders: <...>
**What counts as an answer:** <the confirmatory criterion, stated qualitatively here; exact decision rules come later in pre-registration>
**Scope & exclusions:** <what is explicitly out of scope>
**Open questions for prior-work survey:** <methods to check, known confounds to look up>
Commit the document to git.
Self-Review — look at the document with fresh eyes:
Fix issues inline. No need to re-review — just fix and move on.
Partner Review Gate — after the self-review passes:
"Framing written and committed to
<path>. Please review it and let me know if you want changes before we survey prior work and design the analysis."
Wait for the response. If they request changes, make them and re-run the self-review. Only proceed once they approve.
Transition: