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Agent skill

Stat Modeling Tools

by DrugClaw in DrugClaw/DrugClaw

Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export.

Apache-2.0Auto-check passedData & Analytics

Install Stat Modeling Tools

skills CLI
$ npx skills add DrugClaw/DrugClaw --skill stat-modeling-tools -a claude-code

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

GitHub CLI
$ gh skill install DrugClaw/DrugClaw stat-modeling-tools --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/DrugClaw/DrugClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/science/stat-modeling-tools .claude/skills/stat-modeling-tools && 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
stat-modeling-tools
GitHub stars
125
Token cost
~879 tokens
SKILL.md length
279 words
Files
3
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export.

  • Works in 5 steps: Identify outcome type first: continuous,… → Run a small deterministic statistical… → Report effect sizes and confidence… → …
  • The user asks for statistical test selection
  • SKILL.md covers Environment Check, Bundled Assets, Preferred Workflow and Hypothesis Tests, plus 3 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Stat Modeling Tools is an agent skill from DrugClaw/DrugClaw. Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export. Use when the user asks for statistical test selection, OLS or logistic regression, coefficient tables, inference, or reproducible statistical summaries for scientific datasets.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `templates/stat_test_report.py` and `templates/statsmodels_regression.py`).

It sits in Data & Analytics, covering Statistics. It works with statsmodels. The repository describes itself as: 💊 AI Research Assistant for Accelerated Drug Discovery. 🦞. The licence is Apache-2.0.

When your agent uses it

  • The user asks for statistical test selection
  • Logistic regression
  • Coefficient tables
  • Reproducible statistical summaries for scientific datasets

Example prompts

  • “/stat-modeling-tools”

Requirements

  • Python 3

Workflow steps

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

  1. Identify outcome type first: continuous, binary, count, or categorical contingency table.
  2. Run a small deterministic statistical summary before fitting a larger model.
  3. Report effect sizes and confidence intervals, not only p-values.
  4. Save CSV and JSON outputs so the result is reusable.
  5. Keep claim scope tied to the study design. Statistical association is not causal proof.

What it can do on your machine

Read from SKILL.md and the folder at commit 960a6e0. 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

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    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

Stat Modeling Tools loads about 879 tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 279 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~879

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 DrugClaw/DrugClaw at commit 960a6e0, republished under its Apache-2.0 licence (© DrugClaw). 279 words, ~879 tokens.

Download SKILL.mdSave it as .claude/skills/stat-modeling-tools/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
stat-modeling-tools
description
Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export. Use when the user asks for statistical test selection, OLS or logistic regression, coefficient tables, inference, or reproducible statistical summaries for scientific datasets.
source
drugclaw
updated_at
2026-03-11

Stat Modeling Tools

Use this skill when the user needs reproducible statistical analysis rather than only visual inspection.

Typical triggers:

  • choose or run a hypothesis test on tabular data
  • compare two groups or test association between variables
  • fit OLS, logistic, or Poisson models with coefficient tables
  • inspect residuals, p-values, confidence intervals, or effect sizes
  • generate machine-readable statistical summaries for a manuscript or report

Environment Check

bash
which python3 || true
python3 - <<'PY'
mods = ["numpy", "pandas", "scipy", "statsmodels"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If key modules are missing, say so explicitly and recommend the optional drug-sandbox image documented in docs/operations/science-runtime.md.

Bundled Assets

  • templates/stat_test_report.py
  • templates/statsmodels_regression.py

Preferred Workflow

  1. Identify outcome type first: continuous, binary, count, or categorical contingency table.
  2. Run a small deterministic statistical summary before fitting a larger model.
  3. Report effect sizes and confidence intervals, not only p-values.
  4. Save CSV and JSON outputs so the result is reusable.
  5. Keep claim scope tied to the study design. Statistical association is not causal proof.

Hypothesis Tests

bash
python3 templates/stat_test_report.py \
  --input stats/assay.csv \
  --test independent_ttest \
  --value-column response \
  --group-column arm \
  --group-a control \
  --group-b treated \
  --output stats/assay_ttest.csv \
  --summary stats/assay_ttest.json

Supported baseline tests in the bundled template:

  • independent_ttest
  • paired_ttest
  • mannwhitney
  • chi_square
  • pearson
  • spearman

Use this for quick but explicit statistical reporting.

Regression With Statsmodels

bash
python3 templates/statsmodels_regression.py \
  --input stats/cohort.csv \
  --model ols \
  --outcome response \
  --feature age \
  --feature dose \
  --feature biomarker \
  --output stats/ols_coefficients.csv \
  --summary stats/ols_summary.json

Supported baseline models in the bundled template:

  • ols
  • logit
  • poisson

Use this for:

  • coefficient tables with confidence intervals
  • basic inference and model-fit summaries
  • prediction export for downstream review

Working Rules

  • Prefer exact test names and explicit group labels.
  • Check whether the data are paired before running paired tests.
  • For regression, list the exact feature set and reference coding assumptions.
  • Do not oversell significance when effect sizes are trivial.
  • Distinguish exploratory testing from pre-specified confirmatory analysis.

For Kaplan-Meier, Cox models, and time-to-event workflows, activate survival-analysis-tools. For static or interactive figures, activate scientific-visualization-tools. For study design, reproducibility planning, or manuscript critique, activate scientific-workflow-tools or clinical-research-tools.

© DrugClaw, 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 2 other files in skills/science/stat-modeling-tools of DrugClaw/DrugClaw.

  • SKILL.md
  • templates/stat_test_report.py
  • templates/statsmodels_regression.py

Open the folder on GitHubat commit 960a6e0

Compare with similar skills

Stat Modeling Tools next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Stat Modeling Tools compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Stat Modeling Tools this skillDrugClaw/DrugClaw125—~879Automated safety check: PassApache-2.0
Statistical Analysisspacering-net/codeg3.8k4 repos~5kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.6k16 repos~4.9kAutomated safety check: PassBSD-3-Clause
Statistical Data Analysislingzhi227/agent-research-skills384—~886Automated safety check: PassNone
Quant Statistical MethodsHKUDS/Vibe-Trading35k—~4kAutomated safety check: PassMIT
StatsmodelsK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: NotesBSD-3-Clause

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Works with

Categories

Questions about Stat Modeling Tools

What does Stat Modeling Tools do?

Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export. Stat Modeling Tools is an agent skill from DrugClaw/DrugClaw. Statistical modeling workflow guide for hypothesis tests, effect-size reporting, statsmodels regression, diagnostics, and structured result export.

When should I use Stat Modeling Tools?

Stat Modeling Tools fits situations like: the user asks for statistical test selection; logistic regression; coefficient tables; reproducible statistical summaries for scientific datasets.

How do I install Stat Modeling Tools in Claude Code?

Run `npx skills add DrugClaw/DrugClaw --skill stat-modeling-tools -a claude-code`. Or copy the skill folder (skills/science/stat-modeling-tools in DrugClaw/DrugClaw) into .claude/skills/stat-modeling-tools in your project. Claude Code loads it when a task matches its description.

How do I install Stat Modeling Tools in Codex?

Run `npx skills add DrugClaw/DrugClaw --skill stat-modeling-tools -a codex`. Or copy the skill folder (skills/science/stat-modeling-tools in DrugClaw/DrugClaw) into .agents/skills/stat-modeling-tools in your project. Codex loads it when a task matches its description.

Can I use Stat Modeling Tools 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 DrugClaw/DrugClaw --skill stat-modeling-tools -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stat-modeling-tools, .gemini/skills/stat-modeling-tools, .github/skills/stat-modeling-tools and .opencode/skills/stat-modeling-tools in your project.

What does Stat Modeling Tools need to run?

Going by SKILL.md and its folder, Stat Modeling Tools needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Stat Modeling Tools 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 Stat Modeling Tools 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 Stat Modeling Tools use?

Stat Modeling Tools 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 Stat Modeling Tools use?

About 879 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Stat Modeling Tools?

Skills that share tags, products or a category with Stat Modeling Tools: Statistical Analysis (spacering-net/codeg, 3.8k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars), Statistical Data Analysis (lingzhi227/agent-research-skills, 384 stars) and Quant Statistical Methods (HKUDS/Vibe-Trading, 35k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stat Modeling Tools?

DrugClaw (a GitHub organization) maintains it in DrugClaw/DrugClaw, which has 125 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on March 23, 2026.

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