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statsmodels

Skill3 starsChanged 8 months agoMITView on GitHub

Fit statistical models with detailed inference and diagnostics for CS experiments, telemetry, time series, and structured evaluations. Use when you need OLS, GLM, mixed models, logistic regression, count models, or time-series analysis with coefficient tables, residual checks, and interpretable model summaries.

14 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 14 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when the task requires explicit statistical modeling and interpretable diagnostics rather than black-box prediction.

    line 8
  2. Distinctive:

    Prefer simpler models when interpretation is the goal.

    line 30
  3. Very specific:

    Use statistical-analysis first if the main task is test selection rather than model fitting.

    line 31
  4. Distinctive:

    When the user asks for modeling help without more detail, return:

    line 35

“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “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

Statsmodels

Use this skill when the task requires explicit statistical modeling and interpretable diagnostics rather than black-box prediction.

Good Fits

  • regression on experimental factors or system settings
  • logistic or count models for outcome analysis
  • mixed models for repeated measurements or participant-level data
  • time-series analysis for latency, throughput, or operational logs
  • inference-heavy analyses where coefficient interpretation matters

Local References

  • references/linear_models.md
  • references/glm.md
  • references/discrete_choice.md
  • references/stats_diagnostics.md
  • references/time_series.md

Working Rules

  • Match the model family to the data-generating process.
  • Inspect residuals and diagnostics before trusting coefficients.
  • Prefer simpler models when interpretation is the goal.
  • Use statistical-analysis first if the main task is test selection rather than model fitting.

Default Output

When the user asks for modeling help without more detail, return:

  1. model family recommendation
  2. required variables and assumptions
  3. diagnostics to inspect
  4. how to report the results clearly

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