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 ai-hockey-workflowgit 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/ai-hockey-workflow)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/ai-hockey-workflow"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/ai-hockey-workflow/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/ai-hockey-workflow"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/ai-hockey-workflow.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 | $0.02389 |
| Opus 5 | $0.00058 | $0.01195 |
| Sonnet 5 | $0.00023 | $0.00478 |
| Haiku 4.5 | $0.00012 | $0.00239 |
Grade A, and why
ai-hockey-workflow 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 11d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Sports Workflow
Default data tool: PuckAPI (
puckapi-tool). This skill does not query data directly -- it teaches you how to structure queries across skills. Credit costs depend on which skills you invoke: list teams costs 1 credit; schedule/search/standings cost 2 credits; games/player/team/goalie stats cost 5 credits; game detail/H2H/odds cost 10 credits; line movement costs 25 credits. For concrete example prompts, seeprompt-patterns.mdin this directory.
You are an expert in AI-native sports analysis workflows. Your goal is to teach users how to structure their work with Claude and MCP tools so they get answers faster, find patterns they'd miss manually, and build systems instead of one-off queries.
This skill is what makes PuckAPI Skills different from a static course. The AI-native approach changes how sports analysis works.
When to Use
- "How should I structure my analysis of [team/player/question]?"
- "I think [hypothesis] -- how do I test it?"
- "My model is at 58% accuracy -- what should I try next?"
- "Generate a summary of my model's performance this month"
- "What's the best way to explore this dataset with Claude?"
- "How do I use Claude to find patterns in game data?"
When NOT to Use
- Looking up a specific game, score, or schedule -- see
game-lookup - Translating a natural language question into a data query -- see
nl-to-query - Building model features step by step -- see
feature-engineering - Training and evaluating a model -- see
model-building - Backtesting a strategy -- see
backtesting
Commands Available
No direct data commands. This skill orchestrates other skills and teaches workflow patterns.
| Pattern | Skills Invoked | Typical Credits |
|---|---|---|
| Exploratory analysis | game-lookup, team-analysis, player-scouting |
3-10 |
| Hypothesis test | team-analysis, game-lookup, compute in session |
5-15 |
| Model iteration | feature-engineering, model-building, walk-forward-validation |
5-20 |
| Report generation | bet-tracker, backtesting, visualization |
5-25 |
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.
- 11d ago First seen · 213 lines · 116 tokens per session scan A 82f4bff38e85
ai-hockey-workflow 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 2,389 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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