nature-statistics

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

# Common statistical failure modes

# Figure statistics and legend alignment

# Reviewer checklist for statistical reporting

# Source basis for `nature-statistics`

# Statistical reporting checklist

Audit 0.4.1 · source sha c5a4a9d64275 · 31 Oct 2026Report an issue or request a re-scan

• Publisheryuan1z0825 GitHub userOwner of the repository this skill is read from
• Source folderyuan1z0825/nature-skills/skills/nature-statisticsThe exact folder we audited
• LicenceApache-2.0As the repository states it
• Commit pinned for this verdictc5a4a9d64275A new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Audit or improve manuscript statistical reporting, including experimental units, replication, uncertainty, tests, and figure statistics. Use for 统计审查、统计方法小节、图注统计 and reviewer concerns; compute new analyses only when…

---
name: nature-statistics
description: "Audit or improve manuscript statistical reporting, including experimental units, replication, uncertainty, tests, and figure statistics. Use for 统计审查、统计方法小节、图注统计 and reviewer concerns; compute new analyses only when requested with data."
---

# Nature Statistics Reporting Skill

Use this skill to make manuscript statistics transparent, reproducible, and appropriately bounded. It is a reporting and review skill, not a substitute for a statistician reanalysing raw data unless the user supplies the data and explicitly asks for computation.

## Default stance

- Prioritize design transparency over decorative statistical language.
- Separate three questions: what was measured, what unit was analysed, and what inference was claimed.
- Treat the independent experimental unit as the default `n`; do not silently treat cells, fields of view, repeated readings, spectra, model runs, or technical replicates as independent biological or experimental samples.
- Prefer effect sizes, uncertainty intervals, sample sizes, and exact test definitions over significance-only phrasing.
- State missing information as `AUTHOR_INPUT_NEEDED` instead of inventing sample sizes, tests, software, corrections, exclusion rules, randomization, or blinding.
- If a journal-specific instruction, study-type guideline, or field standard conflicts with this skill, follow the more specific source and mark the source used.

## Accepted inputs

The skill may receive:

- a Statistical analysis / Methods subsection
- Results paragraphs containing test statistics or p values
- figure panels, legends, captions, or source-data notes
- reviewer comments about statistics
- author notes in Chinese or English
- tables of reported comparisons
- raw or summary data, only when the user wants a concrete reanalysis or figure-statistics check

If the input is partial, run a bounded audit and state which parts cannot be assessed.

## Workflow

1. **Classify the task.** Decide whether the user wants audit, rewrite, draft, reviewer-response support, figure-statistics alignment, or data-backed reanalysis.
2. **Extract the design.** Identify groups, treatments, time points, endpoints, blocking factors, repeated measures, randomization, blinding, exclusions, and missing-data handling.
3. **Define `n` and replication.** Separate independent experimental units, biological replicates, technical replicates, repeated measures, cells/fields/subsamples, simulations, and pooled observations.
4. **Map claims to analyses.** For each result claim, record the comparison/model, test family, assumptions, correction strategy, effect estimate, uncertainty, and exact p-value policy.
5. **Check common failure modes.** Use `references/common-failure-modes.md` when the text involves nested data, many comparisons, cell-level measurements, interaction claims, correlations, regression, outliers, small samples, or significance-only reasoning.
6. **Check reporting completeness.** Use `references/statistical-reporting.md` to verify that Methods and Results give enough information for readers and reviewers to understand the analysis.
   If the target is the flagship journal Nature, also use
   `references/nature-article-requirements.md` for its exact tail, `n`, repeat,
   P-value, test-statistic and degrees-of-freedom requirements.
   If the target is Nature Machine Intelligence, also use
   `../nature-shared/journal-formats/nature-machine-intelligence.md` for its
   legend-statistics, source-data, reporting-summary and stage-specific checks.
7. **Align figure statistics.** Use `references/figure-statistics.md` when figure legends, panel labels, stars, error bars, box plots, violin plots, source data, or supplementary figure notes are involved.
8. **Draft or revise.** Produce conservative, ready-to-paste text. Keep claims within the supplied design and evidence. Do not upgrade statistical association into mechanism or causality.
9. **Run final QA.** Use `references/reviewer-checklist.md` before final delivery for severity labels, unresolved author questions, and reviewer-facing risk.

## Output format

Unless the user asks for another format, return:

```text
Statistics review scope
- Input reviewed:
- Boundary / missing materials:
- Study design readout:
- Independent unit and replication readout:

Major statistical issues
- [P0/P1/P2] Issue:
  Evidence from supplied text:
  Why it matters:
  Fix:

Ready-to-paste revision
[Rewritten Statistical analysis / Results / figure legend text]

AUTHOR_INPUT_NEEDED
- [short factual questions only]

Reviewer-risk note
- What a statistical reviewer may still challenge:
```

For a clean drafting request with enough information, skip the long issue list and return:

```text
Draft Statistical analysis
[ready-to-paste text]

Reporting notes
- n definition:
- tests/models:
- multiple comparisons:
- software/version:
- unresolved fields:
```

## Red lines

- Do not invent p values, sample sizes, degrees of freedom, confidence intervals, software versions, correction methods, preregistration, exclusion rules, or power calculations.
- Do not recommend a statistical test as final when the unit of analysis or design is unclear.
- Do not accept `n = number of cells/images/measurements` as independent replication without checking the experimental hierarchy.
- Do not use “significant” as a synonym for important, large, causal, or biologically meaningful.
- Do not hide non-significant or weak results by rewriting them into stronger claims.
- Do not give medical, regulatory, or clinical-trial statistical advice beyond reporting checks unless the user provides the relevant protocol and asks for bounded manuscript wording.

## Related files

| File | Open when |
|---|---|
| [references/source-basis.md](references/source-basis.md) | You need the source hierarchy or want to justify why the skill emphasizes transparency, reproducibility, and design reporting |
| [references/nature-article-requirements.md](references/

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/yuan1z0825/nature-skills
git -C nature-skills checkout c5a4a9d64275

Works in

1. 2026-10-31
   ✕BLOCK D 69 / 100

   first audit · source c5a4a9d64275

Questions about nature-statistics

• github.com/yuan1z0825/nature-skills/tree/main/skills/nature-stat — the audited source at commit c5a4a9d64275 and every finding aboveObserved · 31 Oct 2026

Spotted something wrong? Report a correction · Methodology