xg-model-building SKILL.md skillA
xg-model-building from puckapi/claude-sports-analytics is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add puckapi/claude-sports-analytics/xg-model-building@v1.1.0Pins v1.1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- puckapi/claude-sports-analytics
- Path
skills/xg-model-building/SKILL.md- Versions recorded
- 3
- Latest version
- v1.1.0, committed May 6, 2026
- First version recorded
- May 1, 2026
- Size
- 12,439 bytes
- Repository stars
- 3
- License
- MIT
Builds expected goals (xG) models from NHL play-by-play shot event data using XGBoost or LightGBM. Use when user asks about expected goals, xG model, shot quality, building an xG model, xGF%, xGA, rebound detection, rush shot detection, or shot probability. Do not use for applying pre-built xG values to team or game analysis -- see team-analysis or hockey-analytics. Do not use for general game prediction models -- see model-building. Do not use for goalie evaluation using xGA -- see…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# xG Model Building > **Important: PuckAPI does NOT have play-by-play data.** The SDH database contains game-level data (scores, teams, odds, goalie starts) but no event-level shot data, coordinates, or play-by-play events. > > **Primary data source for xG:** The NHL Stats API at `api-web.nhle.com` provides free play-by-play data with shot coordinates, event types, and strength state. No API key or credits required. > > **PuckAPI is useful for:** Validating your xG model output against team-level stats (`get_team_stats`, 5 credits) and goalie stats (`get_goalie_stats`, 5 credits). > > For user's own shot data CSV/JSON: skip external sources, work with the file directly. You are an expert in hockey expected goals modeling. Your goal is to build a shot-level xG model that estimates the probability any given shot results in a goal, controlling for shot quality rather than shot volume. ## When to Use - User asks "how do I build an xG model" - User wants to model shot probability or goal probability from play-by-play data - User wants to compute xGF%, xGA, or expected goals for teams or players - User asks about rebound detection, rush shot detection, or shot angle features …
Read the whole file at its exact version.
How to install
mdr add puckapi/claude-sports-analytics/xg-model-building@sha256:51496bee4f328421The line at the top of this page pins the label v1.1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_eurwz2fgilesokf5)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 3 versions of this file, between May 1, 2026 and May 6, 2026, about one every 2 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.1.0 latest | 2026-05-06 | 3355ba8 | 12,439 B | A | view · diff |
| v1.1.0 | 2026-05-04 | c1ac62a | 12,481 B | A | view · diff |
| v1.0.0 | 2026-05-01 | df6b9a7 | 11,494 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 (12439 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 2026-10-15 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_eurwz2fgilesokf5 GET https://markdownregistry.com/api/v1/resolve?ref=puckapi/claude-sports-analytics/xg-model-building GET https://markdownregistry.com/api/v1/blob/51496bee4f328421ccc0fea0d3797d8c91130d5b4623e8ea7ac3f32be77a4691
Your AI assistant 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.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.