Coral Bleaching & Marine Asset Risk API using Vercel AI SDK and Streaming SST Anomalies
Record-breaking ocean temperatures during El Niño trigger widespread coral reef bleaching, causing catastrophic losses for coastal tourism, marine aquaculture, and natural wave barrier insurance policies across the tropics. Marine asset insurers require real-time streaming APIs to track ocean heat content and alert policyholders before mass bleaching occurs. In this challenge, you will implement a web-native API using Vercel's AI SDK in TypeScript. The system will stream real-time analysis of NOAA Coral Reef Watch sea surface temperature (SST) anomaly feeds directly to client dashboards. Your solution will execute tool calling for spatial SST indexing, calculate Degree Heating Weeks (DHW), and stream structured risk assessments with low latency.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a web API using Vercel AI SDK to stream real-time ocean temperature anomalies and coral bleaching marine asset risk.
How work is evaluated
Evaluates the accuracy of Degree Heating Weeks (DHW) calculations and TypeScript stream response structure.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zod_schema_validation
Streamed object complies strictly with Zod definition
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
dhw_accuracy
Accuracy of Degree Heating Weeks calculation • target: 0.99 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Deploy TypeScript agent streams using Vercel AI SDK (`streamText`, `generateObject`).
Process spatial ocean heat content and Degree Heating Weeks (DHW) metrics.
Implement multi-tool JavaScript/TypeScript functions for environmental data retrieval.
Stream risk evaluations with real-time UI component rendering capabilities.
Reference links and supporting material
Synthetic time-series array of sea surface temperature (SST) anomalies across tropical reef assets.
Challenge setup
Evaluates the accuracy of Degree Heating Weeks (DHW) calculations and TypeScript stream response structure.
Challenge input
JSON with reef_id, weekly_sst_anomalies_c
AI SDK
Mandatory TypeScript framework for streaming AI responses.
Evaluated output
JSON with dhw_index, alert_level, asset_risk_score
- Streamed object complies strictly with Zod definition
- Accuracy of Degree Heating Weeks calculation • target: 0.99 • range: 0-1
- Benchmark: MarineHeatBench
- Dhw Accuracy target: 0.99
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Vercel AI SDK · Required
- VESSL AI · Optional
- Llama 3.2 · Optional
- Vercel AI SDK · Required
Evaluation contract
- zod_schema_validation · Weight 1
- dhw_accuracy · Weight 1
Metrics
- dhw_accuracy · Target 0.99
Benchmarks
- MarineHeatBench
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with reef_id, weekly_sst_anomalies_c
Output: JSON with dhw_index, alert_level, asset_risk_score
Execution harness: JavaScript
Evaluation tools
- AI SDK · Web Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.