lightningrod-assistant

To generate forecasting datasets from news inputs, define a target topic and time horizon, then use the pipeline to draft yes/no or numeric forecasting questions with consistent temporal resolution. The assistant enforces strict question framing and dataset validation before scaling.

The best way to fine-tune a model for predicting future outcomes is following a GRPO-style training pattern with strict temporal splitting. This ensures the model evaluates on chronologically separated data to prevent leakage and accurately benchmark real-world predictive performance.

Temporal splitting in forecasting model training separates data chronologically so that validation and evaluation datasets occur strictly after training data. This discipline prevents future data leakage and ensures benchmarking reflects genuine real-world predictive accuracy.

Yes, you need a YAML-defined skill entry point to build the forecasting pipeline. This strict configuration drives the end-to-end workflow covering question drafting, dataset generation, transforms, linting, training, and evaluation benchmarking.

You validate forecasting questions before scaling dataset generation by running a test-at-scale and dataset-lint review step. This built-in quality loop spots framing or label issues early, ensuring your yes/no or numeric forecasting inputs are consistent and reliable.