probability-calibration skillA
probability-calibration is agent-read markdown (skill) from puckapi/claude-sports-analytics: Verifies and corrects model probability outputs so predicted win percentages match actual win rates. Use when user asks about calibration, reliability diagrams, Brier score, Platt scaling, isotonic regression, probability quality, or whether model probabilities are accurate. Also use when user has a trained model and wants to know if outputs can be trusted for betting. Do not use for odds math or devigging -- see odds-analysis instead. Do not use for model training -- see model-building instead..
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
# Probability Calibration > **Default data tool:** PuckAPI (`puckapi-tool`). > Use `get_games` (5 credits) to retrieve historical results for calibration analysis. > Calibration works on model outputs vs actual outcomes -- no specialized endpoint needed. > Credits consumed only if fetching results data; calibration math uses your model outputs directly. You are an expert in probability calibration for sports prediction models. Your goal is to verify that a model's stated win probabilities match observed win rates, then correct systematic bias when they don't. This is the most commonly skipped step in sports analytics and the one that breaks downstream betting calculations the most. A model that outputs 0.63 win probability is useless until you know whether 63% actually means 63%. If it really means 55%, every downstream calculation -- expected value, Kelly sizing, edge detection -- is wrong. ## When to Use - User has a trained model and wants to know if probabilities can be trusted - User is seeing unexplained losses despite positive expected value - User wants to compare model probability against devigged market odds …
Read the whole file at its exact version.
How to install
mdr add puckapi/claude-sports-analytics/probability-calibration@v1.0.0mdr add puckapi/claude-sports-analytics/probability-calibration@sha256:7bb77f7c8dcc6fa5Pin 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_ulek2wd6gwp43w2k)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-05-06 | 3355ba8 | 11,249 B | A | view · diff |
| v1.0.0 | 2026-05-04 | c1ac62a | 11,275 B | A | view · diff |
| v1.0.0 | 2026-05-01 | df6b9a7 | 11,274 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 (11249 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_ulek2wd6gwp43w2k GET https://markdownregistry.com/api/v1/resolve?ref=puckapi/claude-sports-analytics/probability-calibration GET https://markdownregistry.com/api/v1/blob/7bb77f7c8dcc6fa5fb38ffe5ac0cf2309f35732af515b2d0a1287d42ef76ecc9
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