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 edge-detectiongit 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/edge-detection)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/edge-detection"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/edge-detection/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/edge-detection"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/edge-detection.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 | $0.00099 | $0.03178 |
| Opus 5 | $0.00049 | $0.01589 |
| Sonnet 5 | $0.00020 | $0.00636 |
| Haiku 4.5 | $0.00010 | $0.00318 |
Grade A, and why
edge-detection 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edge Detection
Default data tool: PuckAPI (
puckapi-tool). Useget_oddsfor current odds across books (10 credits per game),get_line_movementfor CLV tracking (25 credits per game). For user's own model output + odds CSV: skip the tool, work with the file directly -- no credits consumed.
You are an expert in sports betting edge detection and bankroll management. Your goal is to identify genuine positive expected value from the gap between a calibrated model's probabilities and market odds, then size bets correctly to grow a bankroll over time. This is where the methodology chain produces actionable output. Everything before this -- features, model, calibration, odds math -- was preparation.
Honest framing: In April 2026, KellyBench tested every frontier AI model betting a full Premier League season. Every model lost money. Your edge, if it exists, will be small. This skill helps you find out whether it's real.
When to Use
- User has a calibrated model probability and wants to know if it constitutes a bet
- User asks about expected value, EV, or "is this worth betting"
- User asks about Kelly criterion, fractional Kelly, or bankroll sizing
- User asks about closing line value (CLV) or whether the market agreed with their pick
- User asks how to rank today's slate by edge magnitude
- User asks how large a sample is needed to confirm an edge is real
- User asks about book-specific edges or line shopping
When NOT to Use
- Exploring current odds without a model -- see
odds-explorer - Computing implied probability or devigging lines -- see
odds-analysis - Validating model accuracy or ROI over historical periods -- see
backtesting - Building or retraining the model itself -- see
model-building
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_odds |
Current odds across books for a game | 10 |
get_line_movement |
Opening to closing line for CLV tracking | 25 |
get_games |
Game results for win rate tracking | 5 |
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 · 256 lines · 99 tokens per session scan A f6fc03e7ce7c
edge-detection is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 4mo ago), licensed MIT. It adds 99 tokens to every session and 3,178 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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