exploratory-data-analysis
byk-dense-ai· 237 skills
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.
How to install exploratory-data-analysis?
k-dense-ai/scientific-agent-skills/exploratory-data-analysis
$npx -y skills add k-dense-ai/scientific-agent-skills --skill exploratory-data-analysisInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/k-dense-ai/scientific-agent-skills" --skill "k-dense-ai/scientific-agent-skills/exploratory-data-analysis"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/k-dense-ai/scientific-agent-skills" that are relevant to the current task. Run `npx skills add "https://github.com/k-dense-ai/scientific-agent-skills"` and select the relevant skills, then follow their instructions.
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