model-building skillA
model-building is agent-read markdown (skill) from puckapi/claude-sports-analytics: Trains and validates prediction models for sports game outcomes. Use when user asks about building a prediction model, training a classifier, choosing between logistic regression or XGBoost, model selection, hyperparameter tuning, feature importance, or ensemble methods. Always uses walk-forward methodology -- refuses k-fold. Do not use for feature construction -- see feature-engineering. Do not use for validating probability calibration -- see probability-calibration. Do not use for xG models s.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Model Building > **Default data tool:** PuckAPI (`puckapi-tool`). > Use `get_games` for historical game results (5 credits per query). > Model training consumes no credits -- credits are spent in data collection and feature engineering upstream. You are an expert in building sports prediction models. Your goal is to train classifiers that produce honest accuracy estimates on held-out walk-forward test folds. Training accuracy is never reported. Walk-forward test accuracy is the only number that matters. ## When to Use - User wants to build a model that predicts game outcomes - User asks which algorithm to use (logistic regression vs random forest vs XGBoost) - User wants to tune hyperparameters - User wants to combine multiple models into an ensemble - User wants to understand which features matter - User has features ready and wants to train ## When NOT to Use - Feature construction -- see `feature-engineering` - Evaluating whether walk-forward methodology is correctly implemented -- see `walk-forward-validation` - Verifying that predicted probabilities are calibrated -- see `probability-calibration` - xG (expected goals) models specifically -- see `xg-model-building` …
Read the whole file at its exact version.
How to install
mdr add puckapi/claude-sports-analytics/model-building@v1.0.0mdr add puckapi/claude-sports-analytics/model-building@sha256:7338cb75c515b67bPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_tfq2kxuiqvjuvlc3)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-05-06 | 3355ba8 | 13,293 B | A | view · diff |
| v1.0.0 | 2026-05-04 | 5c5b790 | 13,319 B | A | view · diff |
| v1.0.0 | 2026-05-04 | c1ac62a | 13,319 B | A | view · diff |
| v1.0.0 | 2026-05-01 | df6b9a7 | 13,319 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (13293 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
puckapi/claude-sports-analytics · 3 stars · license MIT · pushed 2026-09-21 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_tfq2kxuiqvjuvlc3 GET https://markdownregistry.com/api/v1/resolve?ref=puckapi/claude-sports-analytics/model-building GET https://markdownregistry.com/api/v1/blob/7338cb75c515b67b180ecc274d3b4c5a94f844db598d6829d7f42414d75a01f2
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.