Pingouin stat

Skill Exekiel179/pingouin-psych-stats/skills/pingouin-stat

Main Pingouin statistics workflow for psychology research. Use when the user says pingouin-stat or asks to analyze psychology data with Pingouin end-to-end: first clarify research question, variables, assumptions, output format such as Word/PDF/LaTeX, table/figure requirements, then route to analysis skills, run approval checks, and organize final results.From its SKILL.md

Install

npx -y skills add Exekiel179/pingouin-psych-stats --skill pingouin-stat

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

3.3 KB, 673 tokens by cl100k_base, as published. Nobody here has run it

Pingouin Stat

This is the main entry point. Use it instead of asking users to choose individual sub-skills.

Load

Read in order:

../../references/intake-checklist.md
../../references/workflow-index.md
../../references/supervision-gates.md

Read ../../references/archive-contract.md before generating code or a deliverable that must be reproducible. Read ../../references/workflow-contract.md for a resumable multi-command run. Read ../../references/pingouin-optimization.md before generating concrete Pingouin code.

Read ../../references/pingouin-api-quickref.md only when generating code. Read ../../references/apa-output-template.md only when writing final prose or tables.

Workflow

Intake: clarify only missing items that affect the analysis.
Route: choose one or more downstream skills from workflow-index.md.
Screen: use pg-data-screening when data shape, missingness, or assumptions are unknown.
Analyze: call the selected analysis workflow or generate the relevant Pingouin code.
Approve: run pg-analysis-approval before final interpretation.
Organize: use pg-reporting to produce the requested deliverable shape.
Archive: initialize a run under archive/analysis-runs/, save the exact code and numerical outputs, then write the report and S0–S5 audit record.

Intake Contract

If key information is missing, ask a compact question set covering:

Research hypothesis.
Data path or available columns.
Outcome, predictor, subject ID, covariates/mediators/raters/items.
Between/within/repeated/nested structure.
Planned or exploratory tests.
Output format: Markdown, Word/docx, PDF, LaTeX, CSV tables, notebook/script.
Figure/table requirements.
Language and reporting standard.

If the user says "你先决定" or provides enough context, proceed with explicit assumptions.

Routing Output

Before analysis, produce this short plan:

Intake: <known + missing/assumed>.
Route: <skill(s)> -> <Pingouin function(s)>.
Approval gates: S0-S5 will be checked before interpretation.
Deliverable: <format/language/tables/figures>.
Next: <ask question / run screening / generate code>.

Result Assembly

Final result should include only the sections that fit the user's requested format:

Analysis plan.
Data screening summary.
Main statistical result.
Follow-up comparisons.
Figure/table code or rendered table.
APA/Chinese prose.
Approval findings.
Reproducible Python code.

Guardrails

Do not skip approval when producing conclusions.
Do not invent results when no data/output exists.
Do not force Pingouin onto designs requiring mixed-effects models, SEM, count models, survival analysis, or complex survey models.
Use short S0-S5 audit codes by default; expand only failed gates or user-requested rationale.
Do not finish a code-generating run without saving the code, output references, and audit record under one archive run directory.

What ships with it: 1 file

270 B alongside SKILL.md

agents/

openai.yaml270 B