Standardize research coding workflows for experiments, data cleaning, statistics, and visualization.
npx skills add https://github.com/wentorai/Research-Claw --skill coding-sop
Please help me install this Agent Skill. Skill: Coding SOP Source: https://github.com/wentorai/Research-Claw/tree/main/skills/coding-sop Command: npx skills add https://github.com/wentorai/Research-Claw --skill coding-sop
Coding SOP prevents research runs from becoming inconsistent, fragile, or hard to reproduce by standardizing how you execute experiments, clean data, test hypotheses, and produce publication-quality figures.
Give the AI your hypothesis, dataset schema, and desired output (report tables + figures), then ask it to generate a reproducible experiment script that saves all outputs to workspace-relative paths and includes environment snapshots and versioned results.
High-intent search queries and answers about installing and using this skill.
Reproducible Python and R data analysis scripts require standardized workflows enforcing seed control, environment snapshots, and workspace-relative output paths. This approach ensures verifiable results across experiment execution, data cleaning, and statistical testing.
Choosing the right statistical test involves using a decision tree based on your study design. You must report required test statistics, effect sizes, and assumption checks to ensure the validity of your hypothesis testing and publication-ready outputs.
Standardizing research workflows for publication-ready figures requires applying consistent data provenance, validation checkpoints, and journal-quality visualization settings. This includes controlling resolution, formats, labels, and accessibility throughout the data processing pipeline.
Safely executing experiment scripts requires implementing safe execution patterns, verifying outcomes, and documenting contradictions without cherry-picking. You should hypothesize, design variables, implement script templates, and maintain versioned results with required artifacts.
Yes, this approach supports cleaning tabular datasets with missing data and outliers through explicit inspection steps. It enforces missing-data handling rules, dtype and outlier checks, feature engineering, and saving cleaned datasets with validation checkpoints.
Limitations of standardizing reproducibility include the overhead of maintaining environment snapshots and required script artifacts like effect sizes and assumption checks. It requires strict adherence to safe execution patterns and consistent data provenance throughout the research workflow.
K-Dense-AIGenerate testable hypotheses from tabular datasets using LLM-driven workflows.
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bacocoAnalyze datasets with pandas, numpy, matplotlib, and seaborn for insights.
Best6668Generate publication-quality figures and LaTeX-ready snippets from modeling data.
