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Agent skill

Manuscript Statistics Audit

by Yuan1z0825 in Yuan1z0825/nature-skills

Audits or rewrites the statistical reporting in a manuscript: experimental units, replication, tests, uncertainty and figure legends, without inventing missing details.

Apache-2.0Auto-check passedResearch & Science

Install Manuscript Statistics Audit

skills CLI
$ npx skills add Yuan1z0825/nature-skills --skill nature-statistics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yuan1z0825/nature-skills nature-statistics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yuan1z0825/nature-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-statistics .claude/skills/nature-statistics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nature-statistics
GitHub stars
47k
Used in
2 other repos
Token cost
~2.1k tokens
SKILL.md length
848 words
Files
11 (incl. references)
Skills in repo
23
Repo updated
First seen
Licence
Apache-2.0

At a glance

Audits or rewrites the statistical reporting in a manuscript: experimental units, replication, tests, uncertainty and figure legends, without inventing missing details.

  • Works in 9 steps: Classify the task. Decide whether the… → Extract the design. Identify groups,… → Define n and replication. Separate… → …
  • Checking a manuscript's statistical methods section before submission
  • SKILL.md covers Default stance, Accepted inputs, Workflow and Output format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill treats statistics as a reporting and review task, not a replacement for a statistician reanalysing raw data. It separates three questions: what was measured, what unit was analysed and what inference was claimed. The independent experimental unit is the default sample size, so cells, fields of view, repeated readings, spectra, model runs or technical replicates are not silently counted as independent samples, and effect sizes and uncertainty intervals are preferred over bare significance wording.

It accepts methods subsections, results paragraphs with test statistics, figure legends, reviewer comments about statistics, comparison tables and notes in Chinese or English, plus raw data only when you ask for a concrete reanalysis. Missing facts such as sample sizes, tests, corrections, exclusions, randomization or blinding are marked `AUTHOR_INPUT_NEEDED` rather than made up. The workflow classifies the task, extracts the design, defines n and replication, and maps each claim to its analysis; the rest is cut off. Reference files cover figure statistics, common failure modes and a reviewer checklist.

When your agent uses it

  • Checking a manuscript's statistical methods section before submission
  • Aligning figure legends with the tests and sample sizes actually used
  • Responding to a reviewer concern about replication or multiple comparisons

Example prompts

  • “Audit the statistics section and figure legends in this draft and flag missing information.”
  • “统计审查：这段结果里的 n 是独立样本吗？请指出重复测量的问题。”
  • “Rewrite the statistical analysis subsection so each test, correction and exact p-value policy is stated.”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Classify the task. Decide whether the user wants audit, rewrite, draft, reviewer-response support, figure-statistics alignment, or…
  2. Extract the design. Identify groups, treatments, time points, endpoints, blocking factors, repeated measures, randomization, blinding…
  3. Define n and replication. Separate independent experimental units, biological replicates, technical replicates, repeated measures…
  4. Map claims to analyses. For each result claim, record the comparison/model, test family, assumptions, correction strategy, effect…
  5. Check common failure modes. Use references/common-failure-modes.md when the text involves nested data, many comparisons, cell-level…
  6. Check reporting completeness. Use references/statistical-reporting.md to verify that Methods and Results give enough information for…
  7. Align figure statistics. Use references/figure-statistics.md when figure legends, panel labels, stars, error bars, box plots, violin…
  8. Draft or revise. Produce conservative, ready-to-paste text. Keep claims within the supplied design and evidence. Do not upgrade…
  9. Run final QA. Use references/reviewer-checklist.md before final delivery for severity labels, unresolved author questions, and…

What it can do on your machine

Read from SKILL.md and the folder at commit e605b35. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Manuscript Statistics Audit loads about 2.1k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 848 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Yuan1z0825/nature-skills at commit e605b35, republished under its Apache-2.0 licence (© Yuan1z0825). 848 words, ~2,063 tokens.

Download SKILL.mdSave it as .claude/skills/nature-statistics/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
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:
Show full SKILL.md (356 more words)Show less

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.
FileOpen when
references/source-basis.mdYou need the source hierarchy or want to justify why the skill emphasizes transparency, reproducibility, and design reporting
references/nature-article-requirements.mdThe target is the flagship journal Nature or the user requests its exact statistical submission checklist
../nature-shared/journal-formats/nature-machine-intelligence.mdThe target is Nature Machine Intelligence or NMI-specific legend, source-data, reporting or stage requirements affect the audit
references/statistical-reporting.mdYou are drafting or auditing Statistical analysis, Methods, Results, or Supplementary Methods text
references/common-failure-modes.mdYou see nested measurements, many comparisons, interaction claims, correlation/regression, outliers, tiny samples, or overstrong p-value language
references/figure-statistics.mdYou are checking figure legends, panel statistics, error bars, stars, box/violin plots, source-data notes, or graphical reporting
references/reviewer-checklist.mdYou are finalizing an audit or preparing a reviewer-facing risk summary
../nature-shared/core/consistency-sweep.mdThe same statistic appears in more than one place, or interval terminology is in question: one metric at two precisions across table and text, SD/Std abbreviation drift, confidence interval used where prediction interval is meant, or overlapping error bars described as outperformance

Source hierarchy

Use sources in this order:

  1. User-supplied manuscript, data, protocol, statistical analysis plan, reviewer comments, and journal instructions.
  2. Nature Portfolio reporting standards and reporting-summary requirements.
  3. Nature Methods / Nature Portfolio statistics guidance summarized in references/source-basis.md.
  4. Study-type reporting guidelines where relevant, for example CONSORT, STROBE, PRISMA, ARRIVE, or field-specific community standards.
  5. Conservative statistical reporting practice.

If the supplied material is insufficient for a defensible statistical recommendation, ask for the missing design facts or provide a bounded wording option rather than guessing.

© Yuan1z0825, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in skills/nature-statistics of Yuan1z0825/nature-skills.

  • SKILL.md
  • README.md
  • README_EN.md
  • agents/openai.yaml
  • manifest.yaml
  • references/common-failure-modes.md
  • references/figure-statistics.md
  • references/nature-article-requirements.md
  • references/reviewer-checklist.md
  • references/source-basis.md
  • references/statistical-reporting.md

Open the folder on GitHubat commit e605b35

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Yuan1z0825/nature-skills, which our catalogue first saw on October 7, 2026.

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Manuscript Statistics Audit this skillYuan1z0825/nature-skills47k2 repos~2.1kAutomated safety check: PassApache-2.0
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Restat Referee Strategybrycewang-stanford/Awesome-Journal-Skills1.2k—~1.3kAutomated safety check: PassMIT
Academic Paper Writing PipelineImbad0202/academic-research-skills51k—~16kAutomated safety check: PassCustom licence
Academic Paper ReviewerImbad0202/academic-research-skills51k—~11kAutomated safety check: PassCustom licence

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Categories

Questions about Manuscript Statistics Audit

What does Manuscript Statistics Audit do?

Audits or rewrites the statistical reporting in a manuscript: experimental units, replication, tests, uncertainty and figure legends, without inventing missing details. The skill treats statistics as a reporting and review task, not a replacement for a statistician reanalysing raw data. It separates three questions: what was measured, what unit was analysed and what inference was claimed.

When should I use Manuscript Statistics Audit?

Manuscript Statistics Audit fits situations like: checking a manuscript's statistical methods section before submission; aligning figure legends with the tests and sample sizes actually used; responding to a reviewer concern about replication or multiple comparisons.

How do I install Manuscript Statistics Audit in Claude Code?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-statistics -a claude-code`. Or copy the skill folder (skills/nature-statistics in Yuan1z0825/nature-skills) into .claude/skills/nature-statistics in your project. Claude Code loads it when a task matches its description.

How do I install Manuscript Statistics Audit in Codex?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-statistics -a codex`. Or copy the skill folder (skills/nature-statistics in Yuan1z0825/nature-skills) into .agents/skills/nature-statistics in your project. Codex loads it when a task matches its description.

Can I use Manuscript Statistics Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yuan1z0825/nature-skills --skill nature-statistics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-statistics, .gemini/skills/nature-statistics, .github/skills/nature-statistics and .opencode/skills/nature-statistics in your project.

What does Manuscript Statistics Audit need to run?

SKILL.md names no scripts, command-line tools or credentials: Manuscript Statistics Audit is instructions for the agent only.

Does Manuscript Statistics Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Manuscript Statistics Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Manuscript Statistics Audit use?

Manuscript Statistics Audit is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Manuscript Statistics Audit use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Manuscript Statistics Audit?

Skills that share tags, products or a category with Manuscript Statistics Audit: Mathmodel Skill (handsomeZR-netizen/mathmodel-skill, 292 stars), Jbes Review Process (franklee16/academic-research-skills, 223 stars), Restat Referee Strategy (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Manuscript Statistics Audit?

Yuan1z0825 (a GitHub user) maintains it in Yuan1z0825/nature-skills, which has 47,222 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 11, 2026.

Source: Yuan1z0825/nature-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.