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statistical-modeling-bayes

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Atualizado1 de julho de 2026 às 23:09

Design and critique statistical models with statsmodels, PyMC, survival models, hierarchical models, power analysis, posterior predictive checks, and uncertainty reporting. Use when a request needs principled statistical modeling rather than a generic machine-learning fit.

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Fonte

BaranziniLab

BaranziniLab/biorouter-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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name	statistical-modeling-bayes
description	Design and critique statistical models with statsmodels, PyMC, survival models, hierarchical models, power analysis, posterior predictive checks, and uncertainty reporting. Use when a request needs principled statistical modeling rather than a generic machine-learning fit.
user-invocable	true
license	Apache-2.0

Statistical Modeling and Bayes

Source adaptation: BioRouter-original synthesis inspired by broad Scientific Agent Skills and Auto-Empirical Research Skills capability patterns, rewritten under Apache-2.0. Do not copy upstream text; use this as a native BioRouter routing and quality-control checklist.

When to Use

Use this skill when the user asks for Statistics. Load more specific BioSkills, BRXT extensions, or language skills when the task touches a concrete dataset, file format, package, or platform.

Intake

Collect the minimum context before acting:

Research question or operational goal.
Data source, file format, platform, package, or API involved.
Unit of analysis and expected output.
Privacy, credential, licensing, and compute constraints.
Whether the user wants a plan, executable code, review, or installed-tool workflow.

Native BioRouter Routing

Prefer these BioRouter-native routes before writing new glue code:

Use relevant BRXT extensions when platform/API access is needed.
Use scientific-research for broad research orchestration.
Use citation-temporal-integrity and claim-evidence-integrity for evidence-heavy deliverables.
Use replication-package-audit when outputs must be reproducible.
Use python-scripting, r-scripting, or BioSkills for package-specific code style.

Quality Gates

Before presenting results as final:

State the data provenance and tool/package versions.
Separate exploratory output from confirmatory evidence.
Check that identifiers, coordinates, sample ids, citations, or run ids are preserved.
Identify assumptions that could change the conclusion.
Provide a rerunnable command, notebook cell, or workflow outline when execution is involved.
Mark anything requiring credentials, proprietary data, or external services as unverified unless it was actually checked.

Output Pattern

Return a compact work plan first, then the implementation or review. For each result, include:

Decision made.
Evidence or file/API source.
Failure modes.
Next verification step.

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name	statistical-modeling-bayes
description	Design and critique statistical models with statsmodels, PyMC, survival models, hierarchical models, power analysis, posterior predictive checks, and uncertainty reporting. Use when a request needs principled statistical modeling rather than a generic machine-learning fit.
user-invocable	true
license	Apache-2.0

Statistical Modeling and Bayes

When to Use

Use this skill when the user asks for Statistics. Load more specific BioSkills, BRXT extensions, or language skills when the task touches a concrete dataset, file format, package, or platform.

Intake

Collect the minimum context before acting:

Research question or operational goal.
Data source, file format, platform, package, or API involved.
Unit of analysis and expected output.
Privacy, credential, licensing, and compute constraints.
Whether the user wants a plan, executable code, review, or installed-tool workflow.

Native BioRouter Routing

Prefer these BioRouter-native routes before writing new glue code:

Use relevant BRXT extensions when platform/API access is needed.
Use scientific-research for broad research orchestration.
Use citation-temporal-integrity and claim-evidence-integrity for evidence-heavy deliverables.
Use replication-package-audit when outputs must be reproducible.
Use python-scripting, r-scripting, or BioSkills for package-specific code style.

Quality Gates

Before presenting results as final:

State the data provenance and tool/package versions.
Separate exploratory output from confirmatory evidence.
Check that identifiers, coordinates, sample ids, citations, or run ids are preserved.
Identify assumptions that could change the conclusion.
Provide a rerunnable command, notebook cell, or workflow outline when execution is involved.
Mark anything requiring credentials, proprietary data, or external services as unverified unless it was actually checked.

Output Pattern

Return a compact work plan first, then the implementation or review. For each result, include:

Decision made.
Evidence or file/API source.
Failure modes.
Next verification step.