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 marketsgit 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/markets)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/markets"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/markets/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/markets"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/markets.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 | $0.00140 | $0.02129 |
| Opus 5 | $0.00070 | $0.01064 |
| Sonnet 5 | $0.00028 | $0.00426 |
| Haiku 4.5 | $0.00014 | $0.00213 |
Grade A, and why
markets 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Markets Orchestration
Bridges ESPN live schedules (NBA, NFL, MLB, NHL, WNBA, CFB, CBB) with Kalshi and Polymarket prediction markets. Before writing queries, consult references/api-reference.md for supported sport codes, command parameters, and price normalization formats.
Quick Start
sports-skills markets get_todays_markets --sport=nba
sports-skills markets search_entity --query="Lakers" --sport=nba
sports-skills markets compare_odds --sport=nba --event_id=401234567
sports-skills markets get_sport_markets --sport=nfl
sports-skills markets get_sport_schedule --sport=nba
sports-skills markets normalize_price --price=0.65 --source=polymarket
sports-skills markets evaluate_market --sport=nba --event_id=401234567
sports-skills markets match_markets --sport=mlb --date=2026-06-06
sports-skills markets get_market_price --venue=kalshi --ticker=KXMENWORLDCUP-26-FR
sports-skills markets get_price_history --venue=kalshi --ticker=KXMENWORLDCUP-26-FR --interval=1d
Python SDK:
from sports_skills import markets
markets.get_todays_markets(sport="nba")
markets.search_entity(query="Lakers", sport="nba")
markets.compare_odds(sport="nba", event_id="401234567")
markets.get_sport_markets(sport="nfl")
markets.get_sport_schedule(sport="nba", date="2025-02-26")
markets.normalize_price(price=0.65, source="polymarket")
markets.evaluate_market(sport="nba", event_id="401234567")
markets.match_markets(sport="mlb", date="2026-06-06")
markets.get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-01T12:00:00+00:00")
markets.get_price_history(venue="polymarket", token_id="<token_id>", interval="1h")
CRITICAL: Before Any Query
CRITICAL: Before calling any orchestration command, verify:
- A
sportcode is provided for sport-aware commands (get_todays_markets,compare_odds,get_sport_markets,evaluate_market). - Price sources are identified correctly before normalization:
espn= American odds,polymarket= 0-1 probability,kalshi= 0-100 integer.
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 · 171 lines · 140 tokens per session scan A 8c046c258e9b
markets is a skill published in the GitHub repository machina-sports/sports-skills (218 stars, last pushed 4d ago), licensed MIT. It adds 140 tokens to every session and 2,129 once invoked, about $0.0007 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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