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 skills add PuckAPI/claude-sports-analytics --skill xg-model-buildinggit clone --depth 1 https://github.com/PuckAPI/claude-sports-analyticsWrote 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/puckapi/claude-sports-analytics/xg-model-building)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/xg-model-building"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/xg-model-building/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/xg-model-building"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/xg-model-building.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00120 | $0.03136 |
| Opus 5 | $0.00060 | $0.01568 |
| Sonnet 5 | $0.00024 | $0.00627 |
| Haiku 4.5 | $0.00012 | $0.00314 |
Grade A, and why
xg-model-building 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 10d ago.
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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.comprovides 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
- User wants to replicate or improve upon MoneyPuck or Evolving Hockey xG methodology
- User asks about strength-state-specific (5v5, PP, SH, EN) goal models
When NOT to Use
- Using xG values that already exist -- to analyze teams with pre-built xG, see
team-analysisorhockey-analytics - Predicting game outcomes (win/loss) -- see
model-building - Evaluating goalie quality using xGA -- see
goalie-analysis - General feature engineering for non-xG features -- see
feature-engineering
Data Sources
NHL Stats API (primary, free)
The NHL Stats API provides play-by-play event data for every game:
https://api-web.nhle.com/v1/gamecenter/{gameId}/play-by-play
Each play-by-play response includes shot events with:
- Event type (SHOT, GOAL, MISS, BLOCK)
- x/y coordinates (NHL coordinate system, feet, center ice = 0,0)
- Shot type (wrist, slap, snap, backhand, tip, deflection, wrap-around)
- Strength state (5v5, PP, SH, EN)
- Period and game time
- Shooter and goalie IDs
What ships with it
1 file 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.
- 10d ago First seen · 255 lines · 120 tokens per session scan A 51496bee4f32
xg-model-building is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (3 stars, last pushed 4mo ago), licensed MIT. It adds 120 tokens to every session and 3,136 once invoked, about $0.0006 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 2026-08-31.
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