Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zebbern/claude-code-guide/regression-modelernpx skills add zebbern/claude-code-guide --skill regression-modelergit clone --depth 1 https://github.com/zebbern/claude-code-guideWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zebbern/claude-code-guide/regression-modeler)<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/regression-modeler"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/regression-modeler.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00076 | $0.00951 |
| Opus 5 | $0.00038 | $0.00476 |
| Sonnet 5 | $0.00015 | $0.00190 |
| Haiku 4.5 | $0.00008 | $0.00095 |
Grade A, and why
regression-modeler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
regression-modeler
Automated regression modeling tool — performs linear regression (OLS) or logistic regression (Logit) on tabular data, producing comprehensive statistical results with plain-language interpretation.
Capabilities
| Feature | Description |
|---|---|
| Linear Regression | OLS with coefficients, R², adjusted R², F-test, AIC/BIC, Durbin-Watson |
| Logistic Regression | Logit with coefficients, Odds Ratio, Pseudo R², likelihood ratio test |
| Multicollinearity Detection | VIF values for each predictor with warning levels |
| Plain-Language Interpretation | Clear explanations of what each metric and coefficient means |
| Auto Detection | Automatically switches to logistic regression when the target is binary (0/1) |
Quick Start
# Linear regression: predict price using all numeric columns as predictors
python3 scripts/regression_analyzer.py data.csv --target price
# Logistic regression: predict churn (0/1) with specified features
python3 scripts/regression_analyzer.py users.csv --target churn --features "age,income,tenure"
# Save results to JSON
python3 scripts/regression_analyzer.py data.csv --target sales --output result.json
Detailed Usage
Basic Invocation
python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options]
Specifying Regression Type
# Force linear regression
python3 scripts/regression_analyzer.py data.csv -t y --type linear
# Force logistic regression
python3 scripts/regression_analyzer.py data.csv -t label --type logistic
# Auto-detect (default)
python3 scripts/regression_analyzer.py data.csv -t y --type auto
Selecting Feature Columns
# Manually specify (comma-separated)
python3 scripts/regression_analyzer.py data.csv -t price -f "sqft,bedrooms,bathrooms"
# Omit to automatically use all numeric columns
python3 scripts/regression_analyzer.py data.csv -t price
Parameters
| Parameter | Short | Required | Default | Description |
|---|---|---|---|---|
input |
— | Yes | — | Input file path (CSV/TSV/Excel/JSON) |
--target |
-t |
Yes | — | Target variable (dependent variable) column name |
--features |
-f |
No | All numeric columns | Predictor column names, comma-separated |
--type |
-T |
No | auto |
Regression type: linear / logistic / auto |
--output |
-o |
No | stdout | Output JSON file path |
--no-const |
— | No | false |
Do not add an intercept term |
--keep-na |
— | No | false |
Keep rows with missing values (for debugging) |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 109 lines · 76 tokens per session scan A 28e6d859f1c6
regression-modeler is a skill published in the GitHub repository zebbern/claude-code-guide (4,600 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 951 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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../../../finance/business-investment-advisor/skills/business-investment-advisor/SKILL.md.
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../../../engineering-team/a11y-audit/skills/a11y-audit/SKILL.md.
adversarial-reviewer
../../../engineering-team/skills/adversarial-reviewer/SKILL.md.
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
app-store-optimization
../../../marketing-skill/skills/app-store-optimization/SKILL.md.