data-analysis
Connect exploratory analysis, cleaning, modeling, statistical interpretation, and reproducible execution across tabular and scientific data.
Before you start
Choose your system and agent, then run the command in your terminal. After installation, send the prompts below to your agent.
curl -sSL https://paperskills.com/scripts/paperskills-install.sh | bash -s -- \
--tool codex \
--skills sa.exploratory-data-analysis,sa.polars,sa.dask,sa.statistical-analysis,sa.scientific-visualization \
--registry https://paperskills.com/api/registryStep 1 · sa.exploratory-data-analysis
Task for your agent
Use exploratory-data-analysis to inspect file formats, fields, missingness, outliers, duplicates, units, suspicious batches, and downstream analysis options.Expected outputs
Step 2 · sa.polars
Task for your agent
Use polars to script cleaning, type conversion, aggregation, joins, and derived variables. Explain when to switch to dask if data exceeds memory.Expected outputs
Step 3 · sa.statistical-analysis
Task for your agent
Use statistical-analysis to choose tests/models, check assumptions, and output interpretations, effect sizes, confidence intervals, and visualization suggestions.Expected outputs
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
View skillHigh-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
View skillDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
View skillGuided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
View skillMeta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
View skill