SportMind gives AI agents the intelligence, reasoning, and context to understand sports — the commercial, financial, and competitive signals the industry runs on.
The problem
A general-purpose LLM given a match to analyse produces generic output. It does not know the things that actually change outcomes. SportMind teaches agents exactly those things.
How it works
AI agents are powerful. Without structured sports intelligence they produce generic output. SportMind is the transmission layer — converting raw sporting events into calibrated signals that agents can reason about and act on.
SportMind agents produce intelligence. They do not execute trades, submit votes, or negotiate contracts. The human decision point is architectural — not a limitation.
The library
Load in order — macro → market → sport domain → athlete → fan token → deployment. The six-layer loading architecture remains constant. The intelligence, reasoning, and context within grows continuously — building toward a complete Mind for Sports. Compatible with any LLM. Skills are structured markdown, not API wrappers. Each layer adds intelligence about the domain, reasoning frameworks for interpreting signals, and context for understanding why outcomes happen — not just what happened.
Developer toolkit
Copy-paste templates, 22 agent prompts, and 11 agentic workflow patterns. Three concrete examples of what the toolkit produces:
The library extends beyond these six core layers toward a complete Mind for Sports — fourteen dimensions of Intelligence, Reasoning, Context, Memory, Judgment, Attention, Learning, Integration, Communication, Calibration, Adaptation, Verification, Ethics, and Transparency. Additional intelligence domains include psychological and coaching intelligence, blockchain and on-chain reasoning, odds and market intelligence, sports integrity, stablecoin and CBDC frameworks, fraud verification, and more.
Signal output
Every signal includes not just a direction — but the reasoning chain and context behind it.
Every analysis returns a consistent structured output — direction, confidence score, modifiers applied, and active flags.
{
"direction": "HOME",
"adjusted_score": 72.4,
"sms": 79,
"recommended_action": "ENTER",
"composite_modifier": 1.10,
"confidence_level": "HIGH",
"signal_class": "EXECUTION",
"modifiers_applied": {
"athlete_modifier": 1.10,
"macro_modifier": 1.00,
"venue_modifier": 1.05,
"officiating_modifier": 1.02
},
"flags": {
"lineup_unconfirmed": false,
"ftp_path2_active": true,
"supply_event_type": "REDUCTION",
"macro_override_active": false
}
}Empirical foundation
130 records across 21 sports. All in the repository. Submitted before real matches. Including the wrong ones.
Who uses SportMind
SportMind is the intelligence, reasoning, and context layer — the foundation any AI-powered sports application needs and none of them should have to build from scratch. The possibilities are endless.
Exploring a use-case not listed here? SportMind is an open playground.
See all use-cases →Community
The first 10 external contributors become Founding Calibrators — permanently credited in the library's history. Every record moves a modifier forward.
Verification
Not all sports crypto assets are equal. SportMind maintains a verified registry of official Fan Tokens™ and a fraud risk intelligence framework to help agents and holders distinguish officially licensed tokens from unverified assets.
Contribute
The first 10 external contributors become Founding Calibrators — permanently credited in the library's history.
Exploring a use-case not listed here? SportMind is an open playground. See all use-cases →
The SportMind Suite
Opinionated starting points built on the SportMind intelligence layer. Fork, configure, and ship.
MIT licensed. Compatible with any LLM.
Intelligence, reasoning, and context — ready to deploy.