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 machina-sports/sports-skills --skill bettinggit clone --depth 1 https://github.com/machina-sports/sports-skillsWrote 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/machina-sports/sports-skills/betting)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/betting"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/betting/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/machina-sports/sports-skills/betting"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/betting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00164 | $0.01911 |
| Opus 5 | $0.00082 | $0.00955 |
| Sonnet 5 | $0.00033 | $0.00382 |
| Haiku 4.5 | $0.00016 | $0.00191 |
Grade A, and why
betting 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 12d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Betting Analysis
Before writing queries, consult references/api-reference.md for odds formats, command parameters, and key concepts.
Quick Start
sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160
Python SDK:
from sports_skills import betting
betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)
CRITICAL: Before Any Analysis
CRITICAL: Before calling any analysis command, verify:
- Odds format is correctly identified (american, decimal, or probability).
- ESPN odds are de-vigged with
devigbefore computing edge vs prediction market prices. - This module computes — it does not fetch. Obtain odds from sport-specific skills or polymarket/kalshi first.
Workflows
Compare ESPN vs Polymarket/Kalshi
- Get ESPN moneyline odds (e.g., from
nba get_scoreboard): Home:-150, Away:+130 - Get Polymarket/Kalshi price for the same outcome (e.g., home at
0.52) - De-vig:
devig --odds=-150,+130 --format=american→ Fair: Home 57.9%, Away 42.1% - Compare:
find_edge --fair_prob=0.579 --market_prob=0.52→ Edge: 5.9%, EV: 11.3% - Or all in one step:
evaluate_bet --book_odds=-150,+130 --market_prob=0.52
Arbitrage Detection
- Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49)
find_arbitrage --market_probs=0.48,0.49 --labels=home,away- Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09%
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.
- 12d ago First seen · 144 lines · 164 tokens per session scan A b03000178c99
betting is a skill published in the GitHub repository machina-sports/sports-skills (218 stars, last pushed 4d ago), licensed MIT. It adds 164 tokens to every session and 1,911 once invoked, about $0.0008 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 2026-08-30.
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