Run any Skill in Manus with one click

statistical-modeling-bayes

Stars1

Forks0

UpdatedJuly 1, 2026 at 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.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Run Skill in Manus

Source

BaranziniLab

BaranziniLab/biorouter-skills

View GitHub Repository View Creator Repositories

Download

Run Skill in Manus

Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

SKILL.md

readonly

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.

More from this repository

same repository

computational-fluid-dynamics

BaranziniLab/biorouter-skills

Plan and review computational fluid dynamics workflows with meshes, boundary conditions, turbulence models, OpenFOAM/FluidSim-style solvers, validation cases, convergence checks, and reproducible simulation artifacts. Use when users mention CFD, fluid dynamics, Navier-Stokes, meshing, turbulence, OpenFOAM, or flow simulation.

2026-07-011

arbor-optimization

BaranziniLab/biorouter-skills

Run evaluator-driven hypothesis trees for improving research artifacts, prompts, analyses, and designs. Use when a task needs branch-and-prune exploration, rubric scoring, iterative refinement, or explicit comparison of competing scientific/workflow alternatives.

2026-07-011

autoskill-workflow-discovery

BaranziniLab/biorouter-skills

Detect repeated user workflows and draft native BioRouter skills, hooks, or checklist updates from observed work patterns. Use when users ask to turn repeated work, screen-history patterns, project routines, or recurring guardrails into reusable BioRouter skills.

2026-07-011

bids-neuroimaging

BaranziniLab/biorouter-skills

Plan and audit BIDS neuroimaging datasets, derivatives, validation, metadata, and reproducible preprocessing. Use when users mention BIDS, fMRI, MRI, EEG/MEG/iEEG, DICOM conversion, dataset_description.json, participants.tsv, or neuroimaging derivatives.

2026-07-011

clinical-ai-modeling

BaranziniLab/biorouter-skills

Plan and review clinical AI modeling workflows with EHR cohorts, PyHealth-style tasks, validation, bias checks, calibration, safety boundaries, and no-PHI handling. Use when users ask about clinical prediction models, risk scores, treatment-response ML, medical reports, or clinical model evaluation.

2026-07-011

general-time-series-forecasting

BaranziniLab/biorouter-skills

Plan and review general time-series forecasting with classical models, ML, foundation models, backtesting, leakage checks, and uncertainty. Use for non-omics forecasting, sensor streams, operations, finance, clinical vitals, demand, or irregular temporal data.

2026-07-011

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.