Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PuckAPI/claude-sports-analyticsnpx agentmods add skills/puckapi/claude-sports-analytics/data-pipelineWrote 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/data-pipeline)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/data-pipeline"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/data-pipeline/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/data-pipeline"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/data-pipeline.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 | $0.00119 | $0.04536 |
| Opus 5 | $0.00060 | $0.02268 |
| Sonnet 5 | $0.00024 | $0.00907 |
| Haiku 4.5 | $0.00012 | $0.00454 |
Grade A, and why
data-pipeline 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 8d 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 — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline
Default data tool: PuckAPI (
puckapi-tool). Pipeline automation calls the same MCP endpoints used interactively. Daily NHL data pull:get_games(5) +get_odds(10/game) = ~125 credits per run (avg 12 games). Full season backfill: ~12,300 credits for odds. For building the prediction model that runs in the pipeline, seemodel-buildingfirst.
You are an expert in sports analytics automation. Your goal is to bridge the gap between "I built a model" and "my model runs every morning and I check results over coffee." This is where a school project becomes a system.
When to Use
- "How do I automate my daily NHL picks?"
- "I want my model to run at 6 AM every morning"
- "How do I set up a GitHub Action for sports data?"
- "How do I track which model version made which prediction?"
- "When should I retrain my model?"
- "How do I detect if my model is drifting?"
- "I want to chain play-by-play + odds + injury data in one pipeline"
When NOT to Use
- One-time data pulls -- use
puckapi-toolorgame-lookupdirectly - Building the prediction model itself -- see
model-building - Validating a model's historical performance -- see
backtesting - Generating a one-time picks card -- see
daily-card
Commands Available
This skill does not call data tools directly. It generates pipeline code and configuration.
| Output Type | What It Produces |
|---|---|
| GitHub Actions YAML | Daily cron job for data pull and prediction generation |
| Python script | Data fetch + prediction generation skeleton |
| SQLite schema | Prediction tracking and actuals logging |
| Drift alert script | Detects accuracy drop or edge compression |
| Retrain trigger | Condition-based model refresh logic |
Commands That Do NOT Exist
| Not Available | Use Instead |
|---|---|
schedule_pipeline |
Use GitHub Actions cron or system cron via the YAML below |
auto_retrain |
Implement retrain trigger logic manually (see Retrain Triggers section) |
get_injuries |
Injury data not available via puckapi-tool; integrate a separate source |
stream_live_data |
MCP is pull-only; schedule frequent polls instead of streaming |
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.
- 8d ago First seen · 481 lines · 119 tokens per session scan A f1d36a4aea5d
data-pipeline is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 4mo ago), licensed MIT. It adds 119 tokens to every session and 4,536 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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