Play 80
Biodiversity Monitor
AI-powered species identification from camera trap, drone, and acoustic sensor data.
AI-powered species identification from camera trap, drone, and acoustic sensor data with ecosystem health scoring, population trend tracking, and conservation priority alerts. AI Vision classifies species from camera trap and drone photos, OpenAI synthesizes ecological reports from multi-modal observations, IoT Hub connects remote sensor networks in protected areas, Cosmos DB stores species sighting records with geospatial indexing, and Functions trigger conservation alerts when population thresholds are breached.
Architecture Pattern
Multi-modal biodiversity: vision + acoustic classification - population tracking - conservation alerts
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Biodiversity Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (197 lines), evaluate (121 lines), tune (222 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with Custom Vision + OpenAI inputs + envFile
TuneKit (AI Config)
- config/openai.json - ecological analysis and synthesis prompts
- config/biodiversity.json - species databases, ecosystem models, alert thresholds
- config/guardrails.json - conservation ethics, data sensitivity
- evaluation/eval.py - Species ID accuracy >90%, Population trend detection >85%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.