Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Boom5426/Nature-Paper-Skills --skill stats-reporting-auditgit clone --depth 1 https://github.com/Boom5426/Nature-Paper-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/boom5426/nature-paper-skills/stats-reporting-audit)<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/stats-reporting-audit"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/stats-reporting-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/stats-reporting-audit"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/stats-reporting-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 92 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00141 | $0.01539 |
| Opus 5 | $0.00071 | $0.00770 |
| Sonnet 5 | $0.00028 | $0.00308 |
| Haiku 4.5 | $0.00014 | $0.00154 |
Grade A, and why
stats-reporting-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- nature-statistics — 97% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_NEEDEDinstead 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
- Classify the task. Decide whether the user wants audit, rewrite, draft, reviewer-response support, figure-statistics alignment, or data-backed reanalysis.
- Extract the design. Identify groups, treatments, time points, endpoints, blocking factors, repeated measures, randomization, blinding, exclusions, and missing-data handling.
- Define
nand replication. Separate independent experimental units, biological replicates, technical replicates, repeated measures, cells/fields/subsamples, simulations, and pooled observations. - 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.
- Check common failure modes. Use
references/common-failure-modes.mdwhen the text involves nested data, many comparisons, cell-level measurements, interaction claims, correlations, regression, outliers, small samples, or significance-only reasoning. - Check reporting completeness. Use
references/statistical-reporting.mdto verify that Methods and Results give enough information for readers and reviewers to understand the analysis. - Align figure statistics. Use
references/figure-statistics.mdwhen figure legends, panel labels, stars, error bars, box plots, violin plots, source data, or supplementary figure notes are involved. - 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.
- Run final QA. Use
references/reviewer-checklist.mdbefore final delivery for severity labels, unresolved author questions, and reviewer-facing risk.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 123 lines · 141 tokens per session scan A 65d67b4ac974
stats-reporting-audit is a skill published in the GitHub repository Boom5426/Nature-Paper-Skills (490 stars, last pushed 7d ago), licensed MIT. It adds 141 tokens to every session and 1,539 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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