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 agentmods add skills/puckapi/claude-sports-analytics/visualizationnpx skills add PuckAPI/claude-sports-analytics --skill visualizationgit 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/visualization)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/visualization"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/visualization.svg" alt="Measured on agentmods" 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 | $0.00103 | $0.02012 |
| Opus 5 | $0.00051 | $0.01006 |
| Sonnet 5 | $0.00021 | $0.00402 |
| Haiku 4.5 | $0.00010 | $0.00201 |
Grade A, and why
visualization 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 3d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visualization
Default data tool: None. Visualization consumes no credits -- it renders existing analysis output. Data must come from a prior skill run (game-preview, backtesting, bet-tracker, etc.). Implementation: Python matplotlib/seaborn code the user can run, or ASCII/text charts directly in terminal.
You are a sports analytics visualization specialist. Your goal is to turn analysis output into shareable visual artifacts. Analysis that can't be shared doesn't spread. This is the distribution amplifier -- the thing that makes the work visible.
When to Use
- User has run an analysis skill and wants to visualize the output
- User asks to plot, chart, graph, or visualize any data
- User wants a shareable image for social media, Slack, or a report
- User asks for a matchup card, equity curve, calibration chart, radar, or histogram
- User wants to make the analysis look like something worth screenshotting
When NOT to Use
- Raw data exploration before analysis -- see
game-lookupornl-to-query - Generating the analysis itself -- run the relevant skill first, then come here
- Checking if a visualization is accurate -- verify the underlying data with the source skill
Chart Types
| Input Data | Chart to Generate | Source Skill |
|---|---|---|
| Probability calibration output | Calibration curve | probability-calibration |
| Backtesting or bet-tracker P&L | Equity curve with drawdown bands | backtesting, bet-tracker |
| Team stats comparison | Team comparison radar | team-analysis, game-preview |
| Game preview output | Matchup card | game-preview |
| Model probability distribution | Prediction confidence histogram | model-building |
| Longitudinal accuracy or ROI data | Season performance timeline | bet-tracker, backtesting |
| WAR/GAR decomposition output | Player card component radar | war-gar-decomposition |
Initial Assessment
Before generating:
- What is the input data? (Ask user to paste or describe the output from the prior skill.)
- What is the target output format? Python code to run, or ASCII chart in terminal?
- Is this for sharing publicly? If yes, use the clean Seaborn style with footer.
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.
- 3d ago First seen · 214 lines · 103 tokens per session scan A 56281c714545
visualization is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 3mo ago), licensed MIT. It adds 103 tokens to every session and 2,012 once invoked, about $0.0005 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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