How do I install the Diagnostics skill?

Run npx skillmds@latest add windcleaverdev/diagnostics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Diagnostics safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Diagnostics?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Diagnostics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Diagnostics?

WindcleaverDev (@windcleaverdev) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Diagnostics by windcleaverdev — Agent Skill

Name: Diagnostics
Author: WindcleaverDev

Diagnostics

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.

WindcleaverDev 22c1f1c 11 files · 45.0 KB Updated Sep 17, 2026

File contents

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

---name: diagnosticsdescription: 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.---# diagnosticsRuns 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 directoryOptional:- `--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```bashuv 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 outputRead 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

Frequently asked questions