Preregister

Preregistration

A preregistration is a written commitment to hypotheses, design, and analysis made before the data exist or before the realized focal outcome has been seen. It separates confirmatory from exploratory work and closes off p-hacking, HARKing, and forking paths. This skill writes the document; the user submits it. To critique a preregistration that already exists, use the council skill; this one drafts.

Structure and the MUST/SHOULD/MAY taxonomy adapted from pedrohcgs/claude-code-my-workflow.

The refusal gate

Check this before reading anything else, and check it again if new information arrives mid-draft.

Refuse if the description contains realized results: "we found", "the estimate is", "p =", "the effect was significant", "condition A scored higher", a coefficient, a sample mean, or a figure of results. Say plainly that a preregistration is forward-looking, that writing one now would misrepresent the analysis order, and offer instead to write it up as an exploratory analysis or a methods section, clearly labeled as such.

Refuse in the same way if the user has run the focal analysis on data they hold, even without stating the numbers. The gate is about analysis order, not about whether numbers appear in the prompt. Ask directly: has the focal outcome been analyzed on these data yet? If the answer is yes or evasive, do not write a preregistration.

Two things are not refusals. Preregistering a new confirmatory test on data the user holds but has not examined for that outcome is legitimate; say so in the document under existing data, and state what has already been seen. And a purely exploratory study does not need a preregistration at all, it needs honest labeling, so offer the OSF exploratory variant instead.

Arguments

• --style aspredicted|osf|aea-rct: override the registry choice.
• --input <path>: a study spec to read (Markdown draft, Notion export, planning doc section).
• --format md|tex: output format, default md.
• --no-verify: skip the citation checks in phase 5.

Phase 1. Read the inputs

With --input, read the spec and pull out: research question, hypotheses, data source, design, conditions, sample, primary analysis, any stated paper_type.

Without it, ask for a one-to-three paragraph description of the study. If no description comes back, stop and ask again. Do not write a document made entirely of placeholders.

If the description has no directional hypothesis, ask once. Do not invent a direction.

Phase 2. Pick the registry

Signal	Style
Online vignette, MTurk or Prolific study, short lab experiment, 1 to 3 DVs	aspredicted
Conjoint, eye-tracking, multi-DV survey experiment, complex or stratified sampling	osf
Field experiment or RCT (livestream pilot, retailer pilot, app A/B test)	aea-rct
Observational confirmatory analysis	osf, preanalysis-plan variant
Ambiguous	aspredicted

AEA RCT registration is required for field experiments at AEA journals (lab experiments are exempt) and accepted by Marketing Science and Management Science; either OSF or AEA works for the marketing journals. Clinical trials (ClinicalTrials.gov, ISRCTN) belong in their own registries and are out of scope here. PROSPERO takes only reviews with health outcomes, so it is not a destination for a marketing meta-analysis.

Phase 3. Write the document

Every field gets one flag:

• MUST, the registry will not accept the submission without it
• SHOULD, reviewers and editors expect it even though the form does not force it
• MAY, include when relevant

For any MUST the input did not supply, write [CLARIFY: <specific question>]. Never fill a MUST with plausible-sounding invention.

Common header for all three styles: title, authors, date, version (v0.1 draft, v1.0 at upload), target journal, and a pointer back to the source spec if --input was given.

AsPredicted, the nine questions

1. Have any data been collected for this study already? (MUST, and answer it honestly)
2. What is the main question or hypothesis? (MUST, directional)
3. Key dependent variable(s) and exactly how they are measured (MUST)
4. How many and which conditions participants are assigned to (MUST)
5. Exactly which analyses will test the main hypothesis (MUST, name the estimator)
6. Outliers and exclusions, decided now (MUST)
7. Sample size and stopping rule (MUST, see below)
8. Anything else (SHOULD, put the falsification criterion and power basis here)
9. Name of the study, not the paper (MUST)

OSF preregistration

Study information (title, description, numbered directional hypotheses) · Design plan (study type, blinding, design, randomization) · Sampling plan (existing data and what has been seen of it, collection procedure, sample size, sample size rationale, stopping rule) · Variables (manipulated, measured, indices, manipulation checks) · Analysis plan (statistical models, transformations, inference criteria, data exclusion, missing data, exploratory analyses labeled as exploratory) · Other. All MUST except blinding, indices, and transformations, which are SHOULD where they apply.

AEA RCT Registry

Title and abstract · Status (In development, Ongoing, Completed, Withdrawn) · Trial and intervention start and end dates · Public intervention description (SHOULD; the form does not star it) · Primary outcomes with how each is constructed · Secondary outcomes · Experimental design and details · Randomization method and randomization unit · Whether treatment is clustered · Planned number of clusters, planned number of observations, planned observations per arm · Power calculation with the minimum detectable effect size for the main outcomes (SHOULD; the form marks it optional, but the sample-size section below applies regardless) · IRB name, approval date, approval number (conditional: the form requires them only when the IRB-approval answer says one exists) · Analysis plan attachment (MAY, and it can be embargoed). Everything not marked otherwise is MUST.

Do not merge sections across styles. The registries ask for different things.

Sample size, power, and stopping rule

A preregistration without a number here is not binding, so this field is MUST in every style. Record all of:

• the effect size assumed, and where it comes from: a pilot, a prior study with a citation, or the smallest effect size of interest. Do not power off a published point estimate without saying so, since significance filtering inflates published estimates (Gelman and Carlin 2014 on Type S and Type M errors).
• alpha, target power, and the test being powered
• the resulting target N, per cell for experiments, and cluster N plus within-cluster N for field designs
• the tool: pwr or simr in R, statsmodels.stats.power in Python, or simulation. Simulate rather than use a closed form for clustered or multilevel designs, and carry the ICC and design effect.
• the stopping rule, stated so that it does not depend on the data: a fixed N, a fixed calendar window, or a quota. Optional stopping is allowed only with a sequential design and its correction, named here.

Flag it when the target is an interaction. Data Colada 17 (Simonsohn 2014) gives per-cell multipliers relative to the two-cell study powered for the simple effect: a fully attenuated interaction needs 2x the participants per cell, which across four cells is 4x the total N; a 70% attenuation needs about 4x per cell; a crossover interaction needs only about 1.3-1.5x per cell. Interactions need far more N than people expect, crossovers are the cheapest case, and an attenuated interaction powered at main-effect N is underpowered, so preregistering that N locks in the underpowering.

What would falsify this

MUST in every style, written as its own field even though only OSF has an obvious slot for it. For each hypothesis, state the pattern of data that would count against it: the sign, the confidence interval excluding the region of interest, the manipulation check failing, the predicted moderation not appearing. For any hypothesis predicting no effect, give equivalence bounds and a TOST test (Lakens 2017), because a non-significant test is not evidence of absence.

Writing this field usually exposes a hypothesis that cannot lose. Say so when it happens.

Phase 4. Cross-checks

Run all of these before writing to disk. Each failure becomes a [CLARIFY:] in place, and the document still gets written but is reported as INCOMPLETE with the count of unresolved MUSTs.

• Directionality. Every hypothesis carries a sign: "higher than", "increases", "negatively predicts". "Is associated with" fails. "No effect" passes only with equivalence bounds.
• Estimator named. The analysis plan names a specific estimator, the software, and the outcome variable: fixest::feols(), lme4::lmer(), brms::brm(), lm(), statsmodels.OLS, linearmodels.PanelOLS, or a Stata command. "Regression" alone fails.
• Standard errors specified. State the clustering level and why, or state that classical errors are appropriate. Follow Abadie, Athey, Imbens and Wooldridge (2023 QJE): cluster where the sampling or the treatment assignment is clustered, not by reflex.
• Sample plan numeric, per the section above. "As many as we can recruit" fails.
• Exclusions decided ex ante and stated concretely: attention check failures, completion time under a named threshold, duplicate IPs, incomplete responses. "We will deal with outliers" fails.
• Internal consistency. If randomized, the unit of randomization matches the unit of analysis or the analysis plan handles the clustering. If observational, the identification strategy is named.
• Manipulation checks. At least one is named for any experiment, and the plan says whether failing participants are excluded (decided now, not later).
• Exploratory analyses are in their own section and labeled as exploratory.

Phase 5. Verify the citations

If the document cites prior literature, and --no-verify was not passed, spawn a general-purpose subagent with the Agent tool and this prompt. Pass the citation strings and the claim each one supports, not the draft, so the verifier judges the citation independently.

Two checks run on every citation, each with its own verdict line.

Metadata verification: does the cited paper exist, with these authors, year, and venue? This is the same check bibcheck runs, at prereg scale (a handful of citations against a .bib of hundreds), and it uses bibcheck's verdict vocabulary: PASS/WARN/FAIL. For a full .bib audit, use the bibcheck skill.

Claim support: does the cited paper actually support the sentence citing it? This check lives here; bibcheck declares it out of scope, since bibcheck verifies metadata only.

Verify these citations independently. For each one, run:

  ~/.claude/skills/reading-papers/scripts/paper.py resolve "<citation>" --json

(The script is a uv self-contained script; run the path directly, it handles its own deps.)

Return two rows per citation.

Metadata row: citation | PASS/WARN/FAIL | DOI | note.
  PASS  a record resolves and title, authors, year, and venue all match as written
  WARN  the paper exists but a field drifts (year, venue, author spelling or truncation)
  FAIL  nothing resolves, or the record is a different paper

Claim-support row: citation | SUPPORTED/UNCLEAR/UNSUPPORTED | note, judged from the resolved
record's title and abstract against the claim attached to the citation.
  SUPPORTED    the abstract is consistent with the claim
  UNCLEAR      the abstract does not settle it; say what would (reading the paper, a named
               section or table)
  UNSUPPORTED  the abstract contradicts the claim, or the paper is about something else
  Skip this row for a metadata FAIL; there is no paper to judge.

Do not read or edit any preregistration draft. Report only what resolve returned.

Citations and the claim each supports:
<list>

Surface every WARN, FAIL, UNCLEAR, and UNSUPPORTED in the final report with the offending citation. Do not silently drop one. A metadata FAIL is either a hallucinated cite or a paper the resolver could not reach, and the user has to look at it either way.

Phase 6. Output

Default file: <project>/prereg_<study-slug>_<YYYYMMDD>.md. Markdown is the default because AsPredicted, OSF, and AEA are all web forms the user pastes into, so the field headings should survive a copy and paste. Use --format tex when the preregistration is going into a paper appendix; then keep the preamble minimal and match whatever the target project already does.

Pick <project> by globbing ~/Library/CloudStorage/Dropbox*/Apps/Overleaf/*/ (this setup assumes Overleaf projects sync there via Dropbox; adjust to your machine) and matching the study to a directory. If nothing matches, ask where it should go rather than hiding it in a cache directory.

Report to chat:

Preregistration draft: <path>
  Style: <aspredicted|osf|aea-rct>   Target journal: <...>
  Status: READY | INCOMPLETE (<n> MUST items unresolved)
  [CLARIFY:] placeholders: <n>  ->  <list them>
  Cross-checks failed: <list, or none>
  Citation metadata: <n> PASS, <n> WARN, <n> FAIL  (or "none to verify")
  Claim support: <n> SUPPORTED, <n> UNCLEAR, <n> UNSUPPORTED
  Upload to: aspredicted.org | osf.io/registries | socialscienceregistry.org

Failure modes

• Description contains results, or the focal analysis has already been run. Refuse, per the gate.
• No testable directional hypothesis and the user declines to give one. Switch to the OSF exploratory variant and say in the document header that it is not a confirmatory registration.
• The user wants to amend a preregistration already submitted. Registries handle this through formal amendments with a timestamped trail; out of scope.
• The user asks for the power calculation itself to be run. That is a separate job (an R or Python script); this skill records the inputs and the resulting N, it does not compute them silently.
• Pilot data exists and is being used for the effect size. Say so explicitly in the document and state that the pilot data will be excluded from the confirmatory sample.

Out of scope

Submitting to any registry, clinical trial and meta-analysis registries, editing an already submitted preregistration, and running the analysis itself.

Related: the reading-papers skill resolves and reads any paper cited here; compile-latex builds the .tex output if --format tex was used.