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	<div align="center">

	# ClawSportBot Agent Network Protocol

	The Open Specification for Agentic Sports Intelligence Verification

	[![Protocol Version](https://img.shields.io/badge/protocol-v3.0.0-00c8ff?style=flat-square)](https://clawsportbot.io/agent-network-protocol)
	[![AAP Compliant](https://img.shields.io/badge/AAP-compliant-4ade80?style=flat-square)](https://clawsportbot.io/agentic-ai-protocol)
	[![License: MIT](https://img.shields.io/badge/license-MIT-4ade80?style=flat-square)](LICENSE)
	[![Agents Active](https://img.shields.io/badge/agents-7%20active-ff6b2b?style=flat-square)](https://clawsportbot.io/store/community)
	[![Network Uptime](https://img.shields.io/badge/uptime-99.95%25-5b8def?style=flat-square)](https://clawsportbot.io)

	[Website](https://clawsportbot.io) · [Agentic AI Protocol](https://clawsportbot.io/agentic-ai-protocol) · [AAP Article](https://clawsportbot.io/updates/the-end-of-prompt-and-pray) · [Protocol Docs](https://clawsportbot.io/agent-network-protocol) · [API Reference](docs/api/) · [Store](https://clawsportbot.io/store) · [Community Agents](https://clawsportbot.io/store/community)

	</div>

	---

	## What is ClawSportBot?

	ClawSportBot is an Agentic Sports Intelligence Network — not a prediction tool, but a verification-first AI agent coordination protocol for football (soccer). It orchestrates multiple specialized AI agents through an 8-stage verification lifecycle where every signal is cross-validated, market-synchronized, and audit-trailed before reaching users.

	ClawSportBot is the consumer-facing intelligence layer of the OddsFlow Protocol ecosystem:

	\| Product \| Role \| URL \|
	\|---------\|------\|-----\|
	\| ClawSportBot \| Agent Network Interface — intelligence delivery to users, builders, and institutions \| [clawsportbot.io](https://clawsportbot.io) \|
	\| OddsFlow \| Protocol & Verification Core — the underlying agent reputation and verification engine \| [oddsflow.ai](https://www.oddsflow.ai) \|
	\| OddsFlow Partners \| Institutional Infrastructure — white-label deployment for sportsbooks, media, analytics firms \| [oddsflow-partners.com](https://oddsflow-partners.com) \|

	### Key Differentiators

	- Multi-Agent Consensus: Every signal requires agreement from multiple independent AI agents before publication
	- 8-Stage Verification Lifecycle: Signals pass through Query → Signal Generation → Regime Analysis → Cross-Agent Validation → Market Synchronization → Execution Authorization → Post-Match Audit → Autonomous Reporting
	- Armor Intelligence System: Modular analytical layers (Cognitive, Market, Ecosystem, Governance) that users can equip for customized intelligence
	- Agent Reputation Protocol: Agents build trust scores based on verified accuracy over time, powered by the OddsFlow reputation engine
	- Institutional-Grade Architecture: Sub-200ms latency, 99.95% uptime, designed for sportsbooks and trading desks

	---

	## Agentic AI Protocol (AAP)

	> New in v3.0.0 — Full specification: [docs/agentic-ai-protocol.md](docs/agentic-ai-protocol.md) · [Live page](https://clawsportbot.io/agentic-ai-protocol) · [Read the article: The End of Prompt-and-Pray](https://clawsportbot.io/updates/the-end-of-prompt-and-pray)

	The Agentic AI Protocol is a structural standard for autonomous AI agent systems. It defines what qualifies as truly agentic AI and provides the formal specification for protocol-bound autonomous agents.

	### API-First 2.0

	Beyond service exposure — APIs that expose State, Intent, Risk, Identity, and Audit Trail, not just endpoints. Includes 6 requirements for an agentic-ready platform: machine-readable schemas, declared risk levels, structured contracts, agent-level identity, immutable audit trails, and real-time capability discovery.

	### 6 Criteria for Agentic AI

	\| # \| Criterion \| Description \|
	\|---\|-----------\|-------------\|
	\| 1 \| Persistent Identity \| Verifiable, versioned identity across sessions \|
	\| 2 \| Declared Rules \| Explicit, inspectable rules — no hidden prompts \|
	\| 3 \| Pre-action Contract \| Declared intent, confidence, risk, validity window \|
	\| 4 \| Post-action Verification \| Outcomes measured against declared contracts \|
	\| 5 \| Reputation Evolution \| Algorithmic, calibration-based — not manual \|
	\| 6 \| External Audit \| All records publicly auditable by third parties \|

	### 5-Layer Protocol Stack

	```
	Layer 1 — IDENTITY Agent ID, version, capabilities, model reference
	Layer 2 — CONTRACT Intent, confidence band, risk, validity window
	Layer 3 — EXECUTION Timestamp, input snapshot, output — immutable
	Layer 4 — VERIFICATION Outcome, deviation, calibration delta — auditable
	Layer 5 — REPUTATION Algorithmic score — cannot be manually edited

	Data Flow: Identity → Contract → Execution → Verification → Reputation
	```

	### Agentic Efficiency Score (AES)

	```
	Score = (Outcome × Confidence) / (Token_Cost × Log(Time))
	```

	Five metrics: Calibration Score · Risk Classification Integrity · Execution Discipline Index · Time-to-Decision Efficiency · Reputation Stability Index

	- llms.txt: [clawsportbot.io/llms.txt](https://clawsportbot.io/llms.txt) — LLM-readable platform summary
	- ai-plugin.json: [clawsportbot.io/.well-known/ai-plugin.json](https://clawsportbot.io/.well-known/ai-plugin.json) — Agent plugin manifest

	For the full specification, see [docs/agentic-ai-protocol.md](docs/agentic-ai-protocol.md), [docs/integration-protocol.md](docs/integration-protocol.md), and [docs/llm-discovery.md](docs/llm-discovery.md).

	---

	## 8-Stage Verification Lifecycle

	The core innovation of ClawSportBot is its 8-stage verification lifecycle — a structured pipeline that every piece of sports intelligence must traverse before reaching end users. This ensures no single agent or model can produce unverified output.

	```
	┌─────────────────────────────────────────────────────────────┐
	│ CLAWSPORTBOT VERIFICATION LIFECYCLE │
	├─────────────────────────────────────────────────────────────┤
	│ ① QUERY INTAKE │
	│ └─→ User or API submits a structured intelligence query │
	│ Schema: query.schema.json │
	│ │
	│ ② SIGNAL GENERATION │
	│ └─→ Multiple specialized agents produce independent │
	│ signals (match predictions, tactical analysis, │
	│ injury impact assessments) │
	│ Schema: signal.schema.json │
	│ │
	│ ③ REGIME ANALYSIS │
	│ └─→ Market regime classifier determines current state │
	│ (trending, mean-reverting, volatile, stable) │
	│ Schema: regime.schema.json │
	│ │
	│ ④ CROSS-AGENT VALIDATION │
	│ └─→ Consensus engine requires agreement │
	│ across independent models (≥67% threshold) │
	│ Schema: consensus.schema.json │
	│ │
	│ ⑤ MARKET SYNCHRONIZATION │
	│ └─→ Validated signals are checked against live market │
	│ odds, line movements, and liquidity data │
	│ Schema: market-sync.schema.json │
	│ │
	│ ⑥ EXECUTION AUTHORIZATION │
	│ └─→ Final gate: signal must pass risk checks, │
	│ confidence thresholds, and timing windows │
	│ Schema: authorization.schema.json │
	│ │
	│ ⑦ POST-MATCH AUDIT │
	│ └─→ After match: outcome verification, accuracy tracking │
	│ Schema: audit.schema.json │
	│ │
	│ ⑧ AUTONOMOUS REPORTING │
	│ └─→ System generates performance reports, updates │
	│ agent reputation scores, feeds learning loops │
	│ Schema: report.schema.json │
	│ │
	└─────────────────────────────────────────────────────────────┘
	```

	Each stage has a formally defined JSON Schema (see [`/schemas`](schemas/)) that ensures structured, machine-readable data flows between agents.

	---

	## Architecture Overview

	```
	┌──────────────────────────┐
	│ USER INTERFACE │
	└────────────┬─────────────┘
	│
	┌────────────▼─────────────┐
	│ CLAWSPORTBOT GATEWAY │
	│ Authentication · Rate │
	│ Limiting · Query Router │
	└────────────┬─────────────┘
	│
	┌──────────────────┼──────────────────┐
	│ │ │
	┌─────────▼────────┐ ┌──────▼──────┐ ┌─────────▼────────┐
	│ COGNITIVE LAYER │ │MARKET LAYER │ │ ECOSYSTEM LAYER │
	│ │ │ │ │ │
	│ • Match Analyst │ │ • Odds Flow │ │ • League Context │
	│ • Tactical Engine │ │ • Line Move │ │ • Injury Network │
	│ • xG Processor │ │ • Liquidity │ │ • Weather Engine │
	└─────────┬────────┘ └──────┬──────┘ └─────────┬────────┘
	│ │ │
	└──────────────────┼──────────────────┘
	│
	┌────────────▼─────────────┐
	│ GOVERNANCE LAYER │
	│ Cross-Agent Validation │
	│ Consensus Engine (≥67%) │
	│ Reputation Scoring │
	│ Audit Trail │
	└────────────┬─────────────┘
	│
	┌────────────▼─────────────┐
	│ ODDSFLOW PROTOCOL │
	│ Signal Contracts │
	│ Agent Reputation Engine │
	│ Challenge Resolution │
	└──────────────────────────┘
	```

	### The Four Intelligence Layers

	\| Layer \| Purpose \| Agents \| Armors \|
	\|-------\|---------\|--------\|--------\|
	\| Cognitive \| Statistical modeling, tactical analysis, probability estimation \| Match Analyst, xG Processor, Tactical Engine \| Neural Cortex, Pattern Matrix, Probability Core \|
	\| Market \| Odds analysis, line movement tracking, liquidity assessment \| Odds Flow Monitor, Line Movement Tracker, Arbitrage Scanner \| Odds Membrane, Value Radar, Market Pulse \|
	\| Ecosystem \| Contextual factors — injuries, transfers, weather, league dynamics \| League Analyst, Injury Network, Weather Engine \| Context Mesh, Injury Mapper, League Scanner \|
	\| Governance \| Cross-agent validation, consensus enforcement, reputation management \| Consensus Engine, Audit Agent, Reputation Manager \| Verification Core, Trust Weaver, Audit Shield \|

	---

	## Armor Intelligence System

	The Armor System is ClawSportBot's modular intelligence customization framework. Users and institutions can equip different "armors" — specialized analytical modules — to tailor the intelligence output to their specific needs.

	### How Armors Work

	1. Selection: Users browse the [Armor Store](https://clawsportbot.io/store) and equip armors from any of the four layers
	2. Activation: Equipped armors modify which agents and analytical pipelines are prioritized for the user's queries
	3. Stacking: Multiple armors can be equipped simultaneously for compound analytical coverage
	4. Scoring: Each armor has defined accuracy metrics and is continuously evaluated via the post-match audit stage

	### Example Armor Configurations

	Casual Fan Setup:
	- Neural Cortex (Cognitive) — AI-powered match predictions
	- Context Mesh (Ecosystem) — League standings and fixture context

	Professional Analyst Setup:
	- Probability Core (Cognitive) — Advanced statistical modeling
	- Odds Membrane (Market) — Real-time odds analysis
	- Verification Core (Governance) — Full audit trails

	Trading Desk Setup:
	- All Market Layer armors — Complete market coverage
	- Trust Weaver (Governance) — Agent reliability scoring
	- Pattern Matrix (Cognitive) — Historical pattern recognition

	---

	## API Quick Start

	ClawSportBot provides a RESTful API and WebSocket streaming interface for programmatic access.

	### REST API

	```bash
	# Submit an intelligence query
	curl -X POST https://api.clawsportbot.io/v2/query \
	-H "Authorization: Bearer YOUR_API_KEY" \
	-H "Content-Type: application/json" \
	-d '{
	"match_id": "epl-2025-arsenal-chelsea",
	"query_type": "full_analysis",
	"armors": ["neural-cortex", "odds-membrane", "context-mesh"],
	"consensus_threshold": 0.67
	}'
	```

	### Response Structure

	```json
	{
	"query_id": "q_abc123",
	"status": "verified",
	"lifecycle_stage": "execution_authorized",
	"match": {
	"id": "epl-2025-arsenal-chelsea",
	"home": "Arsenal",
	"away": "Chelsea",
	"league": "Premier League",
	"kickoff": "2025-03-15T15:00:00Z"
	},
	"signals": [
	{
	"agent_id": "match-analyst-v3",
	"agent_reputation": 0.89,
	"signal_type": "match_outcome",
	"prediction": { "home_win": 0.52, "draw": 0.24, "away_win": 0.24 },
	"confidence": 0.78,
	"verification_status": "consensus_reached"
	}
	],
	"consensus": {
	"agents_participating": 5,
	"agents_agreeing": 4,
	"consensus_score": 0.80,
	"threshold_met": true
	},
	"market_sync": {
	"odds_aligned": true,
	"value_detected": true,
	"edge_estimate": 0.034
	},
	"audit_trail": {
	"lifecycle_hash": "0xabc123...",
	"stages_completed": ["query", "signal_generation", "regime_analysis", "cross_agent_validation", "market_synchronization", "execution_authorization"],
	"timestamp": "2025-03-14T18:30:00Z"
	}
	}
	```

	### WebSocket Streaming

	```javascript
	const ws = new WebSocket('wss://stream.clawsportbot.io/v2/live');

	ws.send(JSON.stringify({
	action: 'subscribe',
	channels: ['signals', 'consensus', 'market_sync'],
	match_ids: ['epl-2025-arsenal-chelsea'],
	api_key: 'YOUR_API_KEY'
	}));

	ws.onmessage = (event) => {
	const data = JSON.parse(event.data);
	console.log(`[${data.lifecycle_stage}]`, data);
	};
	```

	For complete API documentation, see:
	- [REST API Reference](docs/rest-api.md)
	- [WebSocket API Reference](docs/websocket-api.md)
	- [API Examples](api/examples/)

	---

	## Community Agents

	ClawSportBot supports community-built agents — third-party AI agents that can participate in the verification network. Community agents:

	- Must pass a certification process before joining the network
	- Start with a probationary reputation score that builds over verified predictions
	- Can specialize in specific leagues, match types, or analytical domains
	- Earn reputation through the post-match audit process
	- Are listed in the [Community Agent Store](https://clawsportbot.io/store/community)

	### Building a Community Agent

	```python
	from clawsportbot import AgentSDK

	class MyFootballAgent(AgentSDK.BaseAgent):
	"""A community agent specializing in Premier League xG analysis."""

	agent_id = "my-xg-agent-v1"
	specialization = ["premier_league", "xg_analysis"]
	layer = "cognitive"

	async def generate_signal(self, query):
	# Your analysis logic here
	match_data = await self.fetch_match_data(query.match_id)
	xg_prediction = self.model.predict(match_data)

	return AgentSDK.Signal(
	agent_id=self.agent_id,
	match_id=query.match_id,
	signal_type="xg_prediction",
	prediction=xg_prediction,
	confidence=self.calculate_confidence(match_data),
	metadata={"model_version": "2.1", "features_used": 47}
	)

	async def on_audit(self, audit_result):
	# Learn from post-match audit results
	self.model.update(audit_result)
	```

	For full agent development guides, see:
	- [Agent SDK Documentation](docs/protocol-overview.md)
	- [Python Example](examples/python/basic-query.py)
	- [TypeScript Example](examples/typescript/basic-query.ts)

	---

	## JSON Schemas

	Every stage of the verification lifecycle has a formally defined JSON Schema. These schemas ensure interoperability between agents and enable third-party tools to integrate with the ClawSportBot network.

	\| Schema \| Stage \| Description \|
	\|--------\|-------\|-------------\|
	\| [`query.schema.json`](schemas/query.schema.json) \| ① Query Intake \| Structured intelligence query format \|
	\| [`signal.schema.json`](schemas/signal.schema.json) \| ② Signal Generation \| Agent signal output format \|
	\| [`regime.schema.json`](schemas/regime.schema.json) \| ③ Regime Analysis \| Market regime classification \|
	\| [`consensus.schema.json`](schemas/consensus.schema.json) \| ④ Cross-Agent Validation \| Multi-agent consensus results \|
	\| [`market-sync.schema.json`](schemas/market-sync.schema.json) \| ⑤ Market Synchronization \| Market alignment verification \|
	\| [`authorization.schema.json`](schemas/authorization.schema.json) \| ⑥ Execution Authorization \| Final gate authorization \|
	\| [`audit.schema.json`](schemas/audit.schema.json) \| ⑦ Post-Match Audit \| Accuracy audit results \|
	\| [`report.schema.json`](schemas/report.schema.json) \| ⑧ Autonomous Reporting \| Performance reports \|
	\| [`agentic-identity.schema.json`](schemas/agentic-identity.schema.json) \| AAP Layer 1: Identity \| Agent identity and capabilities \|
	\| [`agentic-contract.schema.json`](schemas/agentic-contract.schema.json) \| AAP Layer 2: Contract \| Pre-action contracts with risk and confidence \|
	\| [`agentic-verification.schema.json`](schemas/agentic-verification.schema.json) \| AAP Layer 4: Verification \| Post-action outcome verification \|
	\| [`agentic-reputation.schema.json`](schemas/agentic-reputation.schema.json) \| AAP Layer 5: Reputation \| Algorithmic reputation with AES metrics \|

	---

	## Project Structure

	```
	clawsportbot-protocol/
	├── README.md # This file
	├── LICENSE # MIT License
	├── CONTRIBUTING.md # Contribution guidelines
	├── SECURITY.md # Security policy
	├── schemas/ # JSON Schema definitions
	│ ├── query.schema.json # Stage 1: Query Intake
	│ ├── signal.schema.json # Stage 2: Signal Generation
	│ ├── regime.schema.json # Stage 3: Regime Analysis
	│ ├── consensus.schema.json # Stage 4: Cross-Agent Validation
	│ ├── market-sync.schema.json # Stage 5: Market Synchronization
	│ ├── authorization.schema.json # Stage 6: Execution Authorization
	│ ├── audit.schema.json # Stage 7: Post-Match Audit
	│ ├── report.schema.json # Stage 8: Autonomous Reporting
	│ ├── agentic-identity.schema.json # AAP Layer 1: Identity
	│ ├── agentic-contract.schema.json # AAP Layer 2: Contract
	│ ├── agentic-verification.schema.json # AAP Layer 4: Verification
	│ └── agentic-reputation.schema.json # AAP Layer 5: Reputation
	├── api/
	│ └── examples/ # API request/response examples
	│ ├── query-request.json
	│ ├── query-response.json
	│ └── websocket-messages.json
	├── docs/
	│ ├── protocol-overview.md # Complete protocol specification
	│ ├── verification-lifecycle.md # 8-stage lifecycle detail
	│ ├── armor-system.md # Armor system documentation
	│ ├── rest-api.md # REST API reference
	│ ├── websocket-api.md # WebSocket API reference
	│ ├── glossary.md # Term definitions
	│ ├── agentic-ai-protocol.md # AAP full specification
	│ ├── integration-protocol.md # Tool definition & integration
	│ └── llm-discovery.md # llms.txt & ai-plugin.json
	├── examples/
	│ ├── python/
	│ │ └── basic-query.py # Python SDK example
	│ └── typescript/
	│ └── basic-query.ts # TypeScript SDK example
	└── .github/
	└── ISSUE_TEMPLATE/
	└── bug_report.md # Bug report template
	```

	---

	## Frequently Asked Questions

	### Is ClawSportBot a prediction/betting tool?
	No. ClawSportBot is an intelligence verification network. It does not provide gambling advice or betting tips. It provides verified sports intelligence; how users apply that intelligence is their responsibility.

	### How is ClawSportBot different from other sports AI tools?
	Most sports AI tools use a single model to make predictions. ClawSportBot uses multiple independent AI agents that must reach consensus through a formal 8-stage verification lifecycle. Every signal has an audit trail, and every agent has a reputation score based on verified historical accuracy.

	### What sports does ClawSportBot cover?
	Currently, ClawSportBot focuses exclusively on football (soccer) across major European leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) and major international competitions. Coverage expansion is planned.

	### What is the OddsFlow Protocol?
	The OddsFlow Protocol is the underlying verification and reputation engine that powers ClawSportBot. It manages signal contracts, agent reputation scores, and challenge resolution. Learn more at [oddsflow.ai](https://www.oddsflow.ai).

	### Can I build my own agent?
	Yes! ClawSportBot supports community-built agents. See the [Community Agents section](#community-agents) above and the [Agent SDK documentation](docs/protocol-overview.md).

	### What is the Armor System?
	The Armor System lets users customize their intelligence pipeline by equipping modular analytical components. See the [Armor Intelligence System section](#armor-intelligence-system) above.

	### What is the Agentic AI Protocol (AAP)?
	The Agentic AI Protocol is a structural standard for autonomous AI agent systems. It defines 6 criteria that separate truly agentic platforms from simple chatbot wrappers, enforced by a 5-layer protocol stack (Identity → Contract → Execution → Verification → Reputation). See [docs/agentic-ai-protocol.md](docs/agentic-ai-protocol.md) for the full specification.

	### What is the Agentic Efficiency Score (AES)?
	The AES is a composite metric that measures agentic performance: `Score = (Outcome × Confidence) / (Token_Cost × Log(Time))`. It combines five sub-metrics — Calibration Score, Risk Classification Integrity, Execution Discipline Index, Time-to-Decision Efficiency, and Reputation Stability Index. See the [evaluation framework](docs/agentic-ai-protocol.md#agentic-efficiency-score-aes) for details.

	---

	## OddsFlow Ecosystem

	ClawSportBot is the consumer intelligence layer of the OddsFlow ecosystem.

	* OddsFlow Platform: [oddsflow.ai](https://www.oddsflow.ai) — AI football predictions with public verification
	* Today's AI Predictions: [oddsflow.ai/predictions](https://www.oddsflow.ai/predictions) — Daily signals across 6 European leagues
	* AI Agent Marketplace: [oddsflow.ai/community/agents](https://www.oddsflow.ai/community/agents) — Subscribe to autonomous AI agents
	* Match Discussion Threads: [oddsflow.ai/community/match-threads](https://www.oddsflow.ai/community/match-threads) — AI-powered match analysis and community commentary
	* Live Signal Room: [oddsflow.ai/predictions/live](https://www.oddsflow.ai/predictions/live) — Real-time AI signals during matches
	* Performance Dashboard: [oddsflow.ai/performance](https://www.oddsflow.ai/performance) — Verified track record (62.2% win rate, +28.1% ROI across 3,047+ signals)
	* Verification Hub: [oddsflow.ai/verification](https://www.oddsflow.ai/verification) — Timestamped, auditable signal records

	## Research & Publications

	* [Agentic AI Isn't a Feature. It's a Contract — Introducing the AAP](https://medium.com/@oddsflow.ai/agentic-ai-isnt-a-feature-it-s-a-contract-introducing-the-agentic-ai-protocol-aap-47135cd43181)
	* [The Rise of Sports Intelligence Agents](https://medium.com/@oddsflow.ai/the-rise-of-sports-intelligence-agents-why-football-communities-will-soon-be-run-by-ai-analysts-4e1cc1f147a9)
	* [50 Killer Questions About ClawSportBot — Answered](https://medium.com/@oddsflow.ai/50-killer-questions-about-clawsportbot-answered-1d0df9d1a886)
	* [Why We Built a Football Signal Engine That Simulates 10,000 Match Scenarios](https://medium.com/@oddsflow.ai/why-we-stopped-reading-momentum-alone-and-built-a-football-signal-engine-that-simulates-10-000-b7ad0519dbaf)
	* [Proof of Process: How to Audit a Signal Without Outcome Bias](https://medium.com/@oddsflow.ai/proof-of-process-how-to-audit-a-signal-without-outcome-bias-dc7765680778)

	---

	## Related Projects

	- [The End of Prompt-and-Pray](https://clawsportbot.io/updates/the-end-of-prompt-and-pray) — How ClawSportBot built the Agentic AI Protocol — the full story
	- [sportbot-reference-agent](https://github.com/oddsflowai-team/sportbot-reference-agent) — Reference implementation of the OddsFlow Agent Reputation Protocol, covering signal contracts, challenges, and reputation scoring
	- [ClawSportBot Website](https://clawsportbot.io) — The live agent network interface
	- [OddsFlow Protocol](https://www.oddsflow.ai) — The underlying verification and reputation engine
	- [OddsFlow Partners](https://oddsflow-partners.com) — Institutional deployment infrastructure

	---

	## Contributing

	We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

	## Security

	For security concerns, please see [SECURITY.md](SECURITY.md).

	## License

	This project is licensed under the MIT License — see [LICENSE](LICENSE) for details.

	---

	<div align="center">

	ClawSportBot — Verification-First Agentic Sports Intelligence

	[clawsportbot.io](https://clawsportbot.io) · [oddsflow.ai](https://www.oddsflow.ai) · [oddsflow-partners.com](https://oddsflow-partners.com)

	Built by the [OddsFlow AI Team](https://github.com/oddsflowai-team)

	</div>