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 Aperivue/medsci-skills --skill analyze-statsgit clone --depth 1 https://github.com/Aperivue/medsci-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/aperivue/medsci-skills/analyze-stats)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/analyze-stats"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/analyze-stats/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/aperivue/medsci-skills/analyze-stats"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/analyze-stats.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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00056 | $0.14520 |
| Opus 5 | $0.00028 | $0.07260 |
| Sonnet 5 | $0.00011 | $0.02904 |
| Haiku 4.5 | $0.00006 | $0.01452 |
Grade A, and why
analyze-stats 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 721 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis Skill
You are assisting a medical researcher with statistical analyses for medical research papers. Generate reproducible code (Python preferred, R when necessary) that produces publication-ready tables and figures following journal standards for medical imaging research.
Data Privacy Check
Before reading any data file, check whether it might contain Protected Health Information (PHI):
- If
*_deidentified.*files exist in the working directory, use those preferentially. - If only raw CSV/Excel files exist (no
*_deidentified.*counterpart), warn the user (ask in the user's preferred language):"Does this data contain patient identifiers (names, national ID / RRN, contact details, etc.)? If so, please de-identify it first with the
/deidentifyskill." - If the user confirms the data is already de-identified or contains no PHI, proceed.
- NEVER display raw PHI values (names, phone numbers, RRN) in your output. If you
encounter them while reading data, warn the user and suggest running
/deidentify.
Reference Files
- Templates:
${CLAUDE_SKILL_DIR}/references/templates/-- reusable analysis scripts - Analysis guides:
${CLAUDE_SKILL_DIR}/references/analysis_guides/-- on-demand methodology references - Table standards:
${CLAUDE_SKILL_DIR}/references/table-standards/-- journal-specific table formattingtable-standards.md-- universal rules, AMA rules, footnote system, mistakes checklistjournal-profiles/-- YAML profiles per journal (radiology, jama, nejm, lancet, eur_rad, ajr)table-types/-- templates per table type (Table 1, diagnostic accuracy, regression, survival/Cox, agreement/reliability, meta-analysis, model comparison, incremental value, reader study (MRMC))tool-comparison.md-- R/Python tool comparison and recommended pipelines
- Figure style:
${CLAUDE_SKILL_DIR}/references/style/figure_style.mplstyle - Project data: See CLAUDE.md for data locations under
2_Data/
What ships with it
60 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.
- references/analysis_guides/agreement_reliability.md 6.5 KB
- references/analysis_guides/burden_decomposition_forecasting.md 8.8 KB
- references/analysis_guides/calibration.md 6.3 KB
- references/analysis_guides/diagnostic_accuracy.md 9.1 KB
- references/analysis_guides/health_economic_evaluation.md 5.2 KB
- references/analysis_guides/mediation.md 4.5 KB
- references/analysis_guides/mendelian_randomization.md 5.2 KB
- references/analysis_guides/missing_data.md 3.7 KB
- references/analysis_guides/multiplicity.md 5.8 KB
- references/analysis_guides/network_meta_analysis.md 4.0 KB
- references/analysis_guides/nhis_icd10_mapping.md 9.0 KB
- references/analysis_guides/polygenic_risk_score.md 4.3 KB
- references/analysis_guides/propensity_score.md 5.4 KB
- references/analysis_guides/regression.md 4.8 KB
- references/analysis_guides/repeated_measures.md 5.4 KB
- references/analysis_guides/survey_weighted.md 13 KB
- references/analysis_guides/survival.md 7.0 KB
- references/analysis_guides/test_selection.md 3.4 KB
- references/analysis_run_workflow.md 7.5 KB
- references/style/figure_style.mplstyle 1.4 KB
- references/style/theme_publication.R 5.9 KB
- references/table-standards/journal-profiles/ajr.yaml 1.2 KB
- references/table-standards/journal-profiles/european_radiology.yaml 1.3 KB
- references/table-standards/journal-profiles/jama.yaml 1.6 KB
- references/table-standards/journal-profiles/lancet.yaml 1.3 KB
- references/table-standards/journal-profiles/nejm.yaml 1.1 KB
- references/table-standards/journal-profiles/radiology.yaml 1.5 KB
- references/table-standards/table-standards.md 10 KB
- references/table-standards/table-types/agreement.md 3.6 KB
- references/table-standards/table-types/diagnostic_accuracy.md 1.5 KB
- references/table-standards/table-types/incremental_value.md 4.4 KB
- references/table-standards/table-types/meta_analysis.md 2.3 KB
- references/table-standards/table-types/model_comparison.md 1.5 KB
- references/table-standards/table-types/reader_study.md 3.8 KB
- references/table-standards/table-types/regression_results.md 1.8 KB
- references/table-standards/table-types/survival_results.md 4.2 KB
- references/table-standards/table-types/table1_demographics.md 3.8 KB
- references/table-standards/tool-comparison.md 2.7 KB
- references/templates/agreement_analysis.py 14 KB runs code
- references/templates/dca_plot.R 10 KB
- references/templates/diagnostic_accuracy.py 15 KB runs code
- references/templates/dta_meta_analysis.R 19 KB
- references/templates/forest_plot.py 18 KB runs code
- references/templates/likert_summary.py 17 KB runs code
- references/templates/meta_analysis.R 16 KB
- references/templates/propensity_score.py 17 KB runs code
- references/templates/regression.py 14 KB runs code
- references/templates/repeated_measures.py 15 KB runs code
- references/templates/sample_size.R 17 KB
- references/templates/survey_weighted_analysis.py 13 KB runs code
- references/templates/survival_analysis.py 13 KB runs code
- references/templates/table1_demographics.py 10 KB runs code
- scripts/analysis_run_challenge/README.md 761 B
- scripts/analysis_run_challenge/verify.sh 1.5 KB runs code
- scripts/check_generated_code.py 15 KB runs code
- scripts/check_separation.py 11 KB runs code
- scripts/demo_analysis_run.py 2.0 KB runs code
- scripts/rating_monotonicity.py 5.4 KB runs code
- scripts/run_analysis.py 17 KB runs code
- skill.yml 1.8 KB
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.
- 3d ago Changed · -3 lines c541c8811b91
- 11d ago First seen · 724 lines · 56 tokens per session scan A 62fec491713b
analyze-stats is a skill published in the GitHub repository Aperivue/medsci-skills (292 stars, last pushed 3d ago), licensed MIT. It adds 56 tokens to every session and 14,520 once invoked, about $0.0003 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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