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 war-gar-decompositiongit 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/war-gar-decomposition)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/war-gar-decomposition"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/war-gar-decomposition/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/war-gar-decomposition"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/war-gar-decomposition.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 | $0.00116 | $0.03791 |
| Opus 5 | $0.00058 | $0.01895 |
| Sonnet 5 | $0.00023 | $0.00758 |
| Haiku 4.5 | $0.00012 | $0.00379 |
Grade A, and why
war-gar-decomposition 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 9d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WAR/GAR Decomposition
Default data tool: PuckAPI (
puckapi-tool). Useget_game_detailfor game-level data (10 credits per game) andget_player_statsfor player biographical data (5 credits). Note: SDHget_game_detailreturns game info, odds, and goalie starts -- NOT shift-level data. For shift-level data, use the NHL API or public sources (Natural Stat Trick, hockey-reference). Shift-level data is also available free from the NHL API and from public sources (Natural Stat Trick, hockey-reference) -- no credits consumed for those sources. For user's own shift data CSV/JSON: skip the tool, work with the file directly.
You are an expert in advanced hockey player evaluation. Your goal is to compute WAR and GAR components for NHL skaters using RAPM (Regularized Adjusted Plus-Minus) ridge regression, then translate those into contract surplus value analysis and JFresh-style player cards.
When to Use
- User asks about WAR, GAR, RAPM, or wins above replacement for hockey players
- User wants to evaluate player value beyond box score stats
- User asks about contract surplus value or cap efficiency
- User wants to reproduce or extend Evolving Hockey's WAR/GAR methodology
- User wants to build a JFresh-style player card (radar chart of GAR components)
- User asks about regularized adjusted plus-minus or ridge regression for player evaluation
When NOT to Use
- Simple player stats lookup without modeling -- see
player-scouting - Goalie evaluation -- goalies use GSAA (Goals Saved Above Average), not WAR. See
goalie-analysis - Team-level performance and standings analysis -- see
team-analysis - Game prediction or betting models (WAR is a player evaluation metric, not a game prediction feature directly) -- see
model-buildingorfeature-engineering
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_game_detail |
Game metadata, team info, odds records, and goalie starts | 10 |
get_games |
Game list for bulk shift data pulls | 5 |
get_player_stats |
Player biographical data, salary reference | 5 |
get_team_stats |
Team-level validation of RAPM outputs | 5 |
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.
- 9d ago First seen · 299 lines · 116 tokens per session scan A 466183e4590d
war-gar-decomposition is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (3 stars, last pushed 4mo ago), licensed MIT. It adds 116 tokens to every session and 3,791 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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