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python-data

Skill99 starsChanged 6 months agoApache-2.0View on GitHub

Performs heavy numerical compute and statistical analysis inside the sandbox.

  • Installs packages

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run complex calculations, regressions, and aggregations using pandas and scikit-learn in the sandbox.

    line 8
  2. Distinctive:

    Run statistical tests (chi-square, t-test) when appropriate.

    line 17
  3. Very specific:

    Execute in the sandbox: Run via python3 script.py.

    line 18
  4. Distinctive:

    Handle errors: If the code fails, read the traceback and fix it.

    line 19
  5. Very specific:

    Use select_dtypes(include=["object", "str"]) for categorical columns (avoids the pandas 4 deprecation warning).

    line 33

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Python data skill

Run complex calculations, regressions, and aggregations using pandas and scikit-learn in the sandbox.

Workflow

  1. Write a Python script using pandas, numpy, or sklearn to:
    • Join, filter, and aggregate tables.
    • Compute statistics (mean, median, stddev, correlations).
    • Build or evaluate ML models.
    • Run statistical tests (chi-square, t-test) when appropriate.
  2. Execute in the sandbox. Run via python3 script.py.
  3. Handle errors. If the code fails, read the traceback and fix it.
  4. Return structured output. Print results as JSON for downstream skills.

Example output

[
  {"segment": "High Risk", "churn_rate": 0.34, "avg_tenure": 2.1},
  {"segment": "Low Risk",  "churn_rate": 0.05, "avg_tenure": 14.7}
]

Notes

  • Use select_dtypes(include=["object", "str"]) for categorical columns (avoids the pandas 4 deprecation warning).
  • Install missing packages with pip install -q <package> before importing.

Download now (ZIP)

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