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Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats.
Passed 11 of 18 test cases with the skill, and 11 of 18 without it.
2 fixed12 passed either way6 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
When completing an exploratory data analysis for a metabolomics file named `plasma_samples_pos_mode.mzML`, what specific filename should be assigned to the generated markdown report?
| Checked | Without skill | With skill |
|---|---|---|
| Names the generated report file plasma_samples_pos_mode.mzML_eda_report.md. | Missed | Passed |
The instructions your model reads when the skill loads.
Exploratory data analysis (EDA) is the systematic examination of scientific data files to understand their structure, content, quality, and characteristics before formal analysis. This knowhow covers methodology for detecting file types, selecting appropriate analysis approaches, assessing data quality, and generating comprehensive reports across all major scientific data domains.
| Category | Common Formats | Typical Analysis | Key Libraries |
|---|---|---|---|
| Tabular | CSV, TSV, XLSX, Parquet | Summary statistics, distributions, correlations, missing values | pandas, polars |
| Sequence | FASTA, FASTQ, SAM/BAM | Length distribution, quality scores, GC content, alignment stats | BioPython, pysam |
| Image/Microscopy | TIFF, ND2, CZI, DICOM | Dimensions (XYZCT), intensity stats, metadata, calibration | tifffile, aicsimageio, nd2reader |
| Spectral | mzML, SPC, JCAMP, FID | Peak detection, baseline, S/N ratio, resolution | pymzml, nmrglue, pyteomics |
| Structural | PDB, CIF, MOL, SDF | Atom counts, bond validation, B-factors, completeness | BioPython, RDKit, MDAnalysis |
| Array/Tensor | NPY, HDF5, Zarr, NetCDF | Shape, dtype, value range, NaN/Inf check, chunk structure | numpy, h5py, zarr, xarray |
| Omics | H5AD, MTX, VCF, BED | Feature/sample counts, sparsity, annotation completeness | scanpy, pyranges, cyvcf2 |
\x89HDF, GZIP: \x1f\x8b).ome.tiff, .nii.gz, .tar.gz by checking from the rightmost extension inwardData file received
├── What is the file type?
test cases passed, vs 11 / 18 without
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