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Exploratory Data Analysis

Safely profile local scientific data files and draft EDA reports without exposing raw values.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

When installed, the agent can run bounded, local checks on scientific data files you authorise, such as CSV/TSV tables and JSON, plus optional metadata checks on NumPy, HDF5, FASTA/FASTQ and image files. It produces redacted aggregate reports on schema, missing values, group and split structure, and how sensitive results are to outliers and transformations, along with a report template. It is designed not to print raw rows, identifiers or full paths, and to refuse unknown formats; it does not decide what the data means or certify it.

Great for

  • Researchers who want a careful first look at a dataset before modelling or statistical testing
  • Data scientists who need a standard, documented EDA report with missingness and leakage checks
  • Teams handling sensitive scientific data who want outputs that avoid exposing raw values

Skip it if

  • Confirmatory, clinical or causal conclusions: the skill explicitly limits EDA to exploration, so results need separate validation
  • Unsupported or sensitive formats such as Parquet, DICOM or VCF, which are reference-only and need separately validated tools
  • Files outside an approved project root or from untrusted sources where you want the agent to open URLs or archives, which the skill forbids

Try asking

  • “Profile the missingness and group structure of my trial_results.csv file inside /approved/project and draft an EDA report”
  • “Check whether my outcome variable is sensitive to outliers or log transformation before I fit a model”
  • “Inspect the shape and dtype of the arrays in features.npz without loading any object data”
  • “Audit my train/test split columns for possible leakage and summarise the findings in the report template”
Step-by-step guide: how to use Exploratory Data AnalysisInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    exploratory-data-analysis.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip exploratory-data-analysis.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip exploratory-data-analysis.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Set --root to the narrowest approved project folder and keep raw data read-only; the tools write derived outputs separately.
  • Install only the optional libraries you need, in a dedicated environment, and treat the pinned versions as a dated snapshot rather than a full lockfile.
  • Provide a data dictionary, the observational unit, and train/test boundaries before interpreting the output, as the skill requires.

What's inside

  • assets/report_template.md
  • references/bioinformatics_genomics_formats.md
  • references/chemistry_molecular_formats.md
  • references/general_scientific_formats.md
  • references/microscopy_imaging_formats.md
  • references/proteomics_metabolomics_formats.md
  • references/spectroscopy_analytical_formats.md
  • scripts/__init__.py
  • scripts/_capabilities.py
  • scripts/_common.py
  • scripts/_structured.py
  • scripts/_tabular.py
  • scripts/capability_manifest.py
  • scripts/distribution_sensitivity.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.