Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill statistical-analysisgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/statistical-analysis)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/statistical-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/statistical-analysis/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/k-dense-ai/scientific-agent-skills/statistical-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/statistical-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00111 | $0.05869 |
| Opus 5.5 | $0.00044 | $0.02348 |
| Sonnet 5.5 | $0.00022 | $0.01174 |
| Haiku 4.5 | $0.00011 | $0.00587 |
Grade A, and why
statistical-analysis 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 23d 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
3 near-identical copies found in the catalogue:
- statistical-analysis — 97% identical, 142 lines differ
- statistical-analysis — 94% identical, 155 lines differ
- statistical-analysis — 94% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 454 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis
Overview
Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. Define the estimand and sampling units, diagnose model limitations, and report effect estimates with uncertainty. Numerical screens cannot certify assumptions or scientific validity.
When to Use This Skill
Use this skill when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research
Installation
Targets Pingouin 0.7.0, SciPy 1.18.1, statsmodels 0.15.0, PyMC 6.3.2 and ArviZ 1.3.0 (reviewed 2026-10-01). Create a separate environment; do not add mutually incompatible scientific stacks to a shared project environment.
uv venv --python 3.12 .venv-statistics
uv pip install --python .venv-statistics/bin/python "pingouin==0.7.0" "scipy==1.18.1" "statsmodels==0.15.0" pandas matplotlib seaborn
# Optional Bayesian examples:
uv pip install --python .venv-statistics/bin/python "pymc==6.3.2" "arviz==1.3.0"
On Windows use .venv-statistics/Scripts/python.exe. Lock the resolved environment for reproduction.
- Pingouin 0.7 fixes erroneous mixed/repeated-measures corrections, categorical ANOVA, and other results; do not reproduce an old analysis by merely changing its reported version. See the official changelog.
- Pingouin 0.6+ names are
p_val,cohen_d,CI95,p_unc.ttestreports absolute Cohen's d; calculate a signed estimate explicitly. Itspowercolumn is observed power and should not be used to interpret a null result. correction='auto'chooses Welch by unequal sample sizes, not by variance evidence. Setcorrection=Truefor prespecified Welch inference.- PyMC 6 returns an xarray
DataTree. ArviZ 1 usesci_probinsummary/plot_distandprobinhdi; specify 95% intervals explicitly. See Bayesian guidance. - Pingouin omits
BF10for one-sided t-tests; posterior directional probabilities are not Bayes factors. A two-sidedBF10uses its own model/prior and is not the Bayesian counterpart of a Welch unequal-variance model.
What ships with it
6 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.
- 23d ago Changed · -10 lines e112848726b3
- 24d ago Changed 95a80c36e4c9
- 1mo ago First seen · 464 lines · 111 tokens per session scan A 1b1d63fd667e
statistical-analysis is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,470 stars, last pushed 6d ago), licensed MIT. It adds 111 tokens to every session and 5,869 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect a scientific data file and report its structure and quality. Use when triaging an unknown table, array, sequence or image file.