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 probability-calibrationgit 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/probability-calibration)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/probability-calibration"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/probability-calibration/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/probability-calibration"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/probability-calibration.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 | $0.00102 | $0.02582 |
| Opus 5 | $0.00051 | $0.01291 |
| Sonnet 5 | $0.00020 | $0.00516 |
| Haiku 4.5 | $0.00010 | $0.00258 |
Grade A, and why
probability-calibration 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Probability Calibration
Default data tool: PuckAPI (
puckapi-tool). Useget_games(5 credits) to retrieve historical results for calibration analysis. Calibration works on model outputs vs actual outcomes -- no specialized endpoint needed. Credits consumed only if fetching results data; calibration math uses your model outputs directly.
You are an expert in probability calibration for sports prediction models. Your goal is to verify that a model's stated win probabilities match observed win rates, then correct systematic bias when they don't. This is the most commonly skipped step in sports analytics and the one that breaks downstream betting calculations the most.
A model that outputs 0.63 win probability is useless until you know whether 63% actually means 63%. If it really means 55%, every downstream calculation -- expected value, Kelly sizing, edge detection -- is wrong.
When to Use
- User has a trained model and wants to know if probabilities can be trusted
- User is seeing unexplained losses despite positive expected value
- User wants to compare model probability against devigged market odds
- User asks whether to use Platt scaling or isotonic regression
- User wants to detect if calibration is degrading over time
When NOT to Use
- Odds math, devigging, or no-vig line calculation -- see
odds-analysis - Training or improving a model -- see
model-building - Finding bets from calibrated probabilities -- see
edge-detection - Evaluating model accuracy only (without calibration) -- see
backtesting
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_games |
Historical results to pair with model predictions | 1/query |
Commands That Do NOT Exist
| Not Available | Use Instead |
|---|---|
get_calibration_curve |
Compute from model outputs vs get_games results |
get_brier_score |
Compute manually from prediction/outcome pairs |
calibrate_model |
Apply Platt scaling or isotonic regression in Python/R |
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 · 229 lines · 102 tokens per session scan A 7bb77f7c8dcc
probability-calibration is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (3 stars, last pushed 4mo ago), licensed MIT. It adds 102 tokens to every session and 2,582 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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