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WindcleaverDevWindcleaverDev/regkit/.agents/skills/diagnostics/SKILL.md

diagnostics

Skill0 starsChanged 5 months agoMITView on GitHub

Run the full diagnostic battery on a fitted regression model — assumption tests (linearity, homoscedasticity, normality of residuals, independence, multicollinearity), influence analysis (leverage, Cook's distance, DFFITS), and bias/variance assessment via cross-validation. Produces a structured DiagnosticsReport with status flags and ranked remediation recommendations, plus a standalone HTML report. Use this skill whenever a regression has been fitted and the user wants to know if they can trust it, asks "are the assumptions met?", "is this model overfit?", "is this overfitting or underfitting?", or wants to identify influential outliers. Pair with the linear-regression or regularized-regression skills.

3 other projects use a version of this.

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  • Other versions3 projects use a version of this.

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    Wrote their own version · 3

    1. carbon-language/carbon-lang★ 34ksame name
    2. databricks/app-templates★ 218same name
    3. microsoft/webui★ 100same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository5 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 0

    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. Very specific:

    Read the verdict.headline first.

    line 41

“Common” is measured: kept by at least 6 in 10 of the 3 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

diagnostics

Runs the full diagnostic battery on a fitted regression model. Input is the JSON output of a fit skill (e.g. LinearRegressionReport) plus the original data.

When this skill fires

  • User has fitted a regression (via linear-regression or similar) and asks about model quality
  • User asks "is this overfitting?", "are the assumptions met?", "is there multicollinearity?"
  • User asks to identify influential observations, outliers, or high-leverage points
  • The fit skill recommends running diagnostics

Inputs

  • --fit-report <path> — JSON of a LinearRegressionReport (or compatible)
  • --data <path> — the original data file (must contain the same target & features used in the fit)
  • --output <dir> — output directory

Optional:

  • --cv-folds <int> — folds for cross-validation (default 5)
  • --learning-curve — include learning curve data in the report (slower)
  • --test-split <float> — held-out fraction for train/test gap (default 0.2)

How to invoke

uv run python diagnostics/scripts/diagnose.py \
    --fit-report results/report.json \
    --data data/houses.csv \
    --output results/

Outputs results/diagnostics.json (DiagnosticsReport) and results/diagnostics.html.

Verbalising the output

Read the verdict.headline first. Then surface verdict.top_issues in order — each maps to one or more flags. For each flag, the report's recommendations field has the actionable next step. Do not list every assumption check unless asked; lead with what's broken or marginal.

Reference files

  • references/plots_guide.md — what each diagnostic plot should look like
  • references/remediation.md — what to do when each assumption fails

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