https://github.com/sportmind/sportmind
The Sports Intelligence Library For AI Agents
https://github.com/sportmind/sportmind
ai-agents chiliz-chain fan-tokens llm-context mcp open-source sportfi sports-intelligence
Last synced: 2 months ago
JSON representation
The Sports Intelligence Library For AI Agents
- Host: GitHub
- URL: https://github.com/sportmind/sportmind
- Owner: SportMind
- License: mit
- Created: 2026-04-06T12:42:49.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-04-24T22:41:44.000Z (3 months ago)
- Last Synced: 2026-04-25T00:19:22.790Z (3 months ago)
- Topics: ai-agents, chiliz-chain, fan-tokens, llm-context, mcp, open-source, sportfi, sports-intelligence
- Language: Python
- Homepage: https://sportmind.dev
- Size: 2.89 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
README
# SportMind
**The open sports intelligence library for AI agents and developers.**
SportMind teaches AI agents how to reason about sports — not just react to data.
Load a skill, and your agent immediately understands the sport, the athlete,
the commercial landscape, and the external forces acting on it.
[](https://opensource.org/licenses/MIT)
[]()
[]()
[]()
[]()
[]()
---
## Find your starting point in 60 seconds
→ **[WHO-USES-THIS.md](WHO-USES-THIS.md)** — Developer, agent builder, analyst, researcher,
or contributor? This maps you to exactly the files you need.
---
## What problem does this solve?
AI agents that analyse fan tokens, run prediction markets, or power sports GameFi
need more than raw data. They need context:
- That a weigh-in miss in MMA is categorically different from a team losing a regular season game
- That a cricket match on a Mumbai evening will be affected by dew in the second innings
- That a DAXA Investment Warning on a Korean exchange is a staged lifecycle event with
a predictable intervention window — not a binary delisting signal
- That a new Binance listing aligned with a club's actual fanbase geography extends CDI
durability; a misaligned listing does not
- That Galatasaray equity on Borsa Istanbul (GSRAY.IS) leads the GAL fan token by 24–72
hours on commercial news — both instruments are pricing the same underlying entity
- That a liquidity pool with $80k TVL will absorb your signal's value in slippage before you execute
This contextual reasoning is currently rebuilt from scratch by every developer
in the space. **SportMind is the shared layer.**
---
## Five-minute quickstart
**Option A — Paste into any LLM (zero setup)**
```
1. Open Claude, GPT-4, Gemini, Groq, Mistral, or any LLM
2. Paste: contents of core/sportmind-purpose-and-context.md
3. Paste: contents of sports/football/sport-domain-football.md
4. Ask: "PSG vs Arsenal UCL tonight. PSG full squad. Arsenal striker injured.
Using SportMind, generate a pre-match signal."
```
Working in under 3 minutes.
**Option B — Skills API**
```bash
python scripts/sportmind_api.py # start local API
curl "http://localhost:8080/bundle/ftier1-football" # named bundle
curl "http://localhost:8080/stack?sport=football&use_case=fan_token_tier1"
```
**Option C — Clone and run**
```bash
git clone https://github.com/SportMind/SportMind
pip install aiohttp --break-system-packages
python examples/starter-pack/01-simple-signal.py
```
---
## Five layers — one system
| Layer | Directory | What it teaches |
|---|---|---|
| **1 — Sport domain** | `sports/` (42 sports) | How each sport works; event playbooks; risk variables |
| **2 — Athlete intelligence** | `athlete/` (29 sports) | Who is playing; form; composite modifier (0.55–1.25×) |
| **3 — Fan token commercial** | `fan-token/` (64 skills) | Lifecycle; DeFi; governance; exchange intelligence; RWA |
| **4 — Market intelligence** | `market/` (43 docs) | Commercial tier; fanbase; sports equity signals; competition calendar |
| **5 — Macro intelligence** | `macro/` (9 docs) | Crypto cycles; regulatory (MiCA, SEC/CFTC); geopolitical |
**Load order:** macro → market → sport domain → athlete → fan token → output schema
Use a named bundle: `ftier1-football` · `ftier1-cricket` · `prematch-mma` · `governance-brief`
→ `platform/skill-bundles.md` for all 14 bundles with token estimates.
---
## What the library contains
```
579 files · 364 markdown skill files
Sport domain: 42 sports · event playbooks · risk variables · agent reasoning prompts
Athlete: 29 sports · form models · availability · composite modifier (0.55–1.25×)
Fan token: 64 skills · 90 verified tokens (63 active Chiliz + 18 expired + 9 multi-chain)
Lifecycle phases 1–6 · DeFi liquidity · exchange intelligence (EDLI/IPS/RRS)
New listing intelligence · Fan Token Play PATH_2 · governance · KOL influence
Sports equity signals (GSRAY.IS, MANU, JUVE.MI, FWONK, TKO) · CHZ macro layer
Market: 43 documents · club operations · broadcaster intelligence · World Cup 2026
Macro: 9 documents · MiCA · SEC/CFTC joint guidance (March 2026) · US market opening
Core frameworks: 57 files · reasoning patterns · autonomous agent framework · modifier system
breaking news · squad intelligence · historical framework · contextual signals
Platform: 28 files · MCP server (45 tools, 8 servers) · data connectors · API providers
Chiliz Agent Kit · social intelligence · web agent connectors · fraud signals
Community: 177 files · 126 calibration records (96% accuracy, 21 sports) · benchmarks
Developer tools: 11 application blueprints · 13 agentic workflow patterns · 22 agent prompts
3 copy-paste templates · 69 compressed summaries · Skills API · starter pack
```
---
## Agent output format
```json
{
"sportmind_score": {"sms": 79, "sms_tier": "GOOD"},
"signal": {"adjusted_score": 72.4, "direction": "HOME",
"recommended_action": "ENTER"},
"modifiers_applied": {"athlete_modifier": 1.10, "macro_modifier": 1.00,
"composite_modifier": 1.08},
"flags": {"lineup_unconfirmed": false,
"macro_override_active": false}
}
```
Full schema: `core/confidence-output-schema.md`
---
## MCP server
SportMind is available as an MCP tool server — connect any AI agent to the
full library without loading files manually.
```bash
git clone https://github.com/SportMind/SportMind
pip install mcp aiohttp
# stdio (Claude Desktop / Claude Code)
python scripts/sportmind_mcp.py
# HTTP/SSE (hosted agents)
python scripts/sportmind_mcp.py --http --port 3001
```
45 tools across 8 servers: `sportmind_signal` · `sportmind_macro` · `sportmind_stack` ·
`sportmind_fan_token_lookup` · `sportmind_sentiment_snapshot` · `sportmind_pre_match` ·
`sportmind_disciplinary` · `sportmind_governance` · `sportmind_verifiable_source` · and more.
**→ Full deployment guide: [MCP-SERVER.md](MCP-SERVER.md)**
---
## Integration
**Data connections:** `platform/data-connector-templates.md` — lineup data, fan token TVL,
macro state.
**Execution layer:** `platform/chiliz-agent-kit-integration.md` — SportMind intelligence
→ Chiliz Agent Kit → on-chain execution.
**Web agents:** `platform/web-agent-connectors.md` — lineup confirmation (T-2h), PATH_2
supply verification, exchange monitoring, regulatory/macro monitoring.
**MCP deployment:** `platform/sportmind-mcp-deployment.md` — live endpoint in 30 minutes.
**Compatible with:** Claude · GPT-4 · Gemini · LangChain · CrewAI · AutoGen ·
any LLM (skills are structured markdown, not API wrappers).
---
## The calibration record
126 records. 96% accuracy. Zero wrong-direction records outside European football draws.
All records are in `community/calibration-data/` — publicly verifiable, pre-submitted
before real matches. Includes all 5 wrong predictions with root-cause analysis.
Not cherry-picked.
Eight modifiers with zero wrong-direction records across their entire evidence base:
qualifying_delta (F1) · india_pakistan ×2.00 · morning_skate (NHL) · dew_factor (cricket) ·
taper_modifier (swimming) · raider_primacy (kabaddi) · goalkeeper_save_rate (handball) ·
superspeedway_specialist (NASCAR)
---
## Contributing
**The fastest contribution: one calibration record.** No coding required.
See **[FIRST-RECORD-CHALLENGE.md](FIRST-RECORD-CHALLENGE.md)**.
What the community needs most:
- Football calibration records (athlete_modifier: 25/50 threshold)
- Cricket dew_factor records (evening T20 matches)
- Records from any underrepresented sport (rowing, netball, kabaddi)
Full process: `community/calibration-data/CONTRIBUTING.md`
Recognition: `community/CONTRIBUTORS.md`
---
## License
MIT — free to use, modify, and redistribute for any purpose.
---
*WHO-USES-THIS.md → FIRST-RECORD-CHALLENGE.md → examples/starter-pack/*
*SportMind is an independent open-source project. Not affiliated with Chiliz,
Socios, or any sports data provider, though designed to complement them.*