Generate time-series forecasting datasets from public, file, or web sources and fine-tune models using SFT or GRPO patterns, with built-in pipeline validation and experiment tracking.
BigQuery seed sourcing patterns for Lightningrod. Use when sourcing seeds from BigQuery tables.
Production examples for content learning (SFT) training -- survival field guide (TopicTree + WebSearch), medical textbooks (FileSet + QuestionAndLabel). Use when teaching domain knowledge via Q&A pairs and SFT.
Seed generation from user-provided files and custom datasets. Use when converting local files, CSVs, PDFs, or user uploads into Lightningrod seeds.
Decision tree for choosing a training pattern (forward-looking GRPO, content learning SFT, tabular data). Use when starting a new project, choosing between RL and SFT, or selecting an answer type.
Standardised training-experiment tracking. One notebook per experiment under `./userland/<project>/experiments/`, plus a single `experiments.md` index table at the project root. Use whenever a training run's tracked config differs from the previous run.
Uses power tools
Uses Bash, Write, or Edit tools
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Foresight returns a calibrated probability for any question about the future through an OpenAI-compatible API.
Trusted for high-stakes predictions by Numinous, Shore Capital Partners, Awardable, ERIS Marketplace, and others. Foresight processes billions of tokens and serves 100k+ inference requests every day.
Documentation · Get an API key · Research paper
Foresight is served behind an OpenAI-compatible endpoint, so any OpenAI client works — just point base_url at Lightning Rod.
from openai import OpenAI
client = OpenAI(
api_key="your-api-key",
base_url="https://api.lightningrod.ai/v1/openai",
)
completion = client.chat.completions.create(
model="foresight-v4",
messages=[
{"role": "user", "content": "Will the Fed cut rates at its next meeting?"},
],
extra_body={"research": True}, # Auto research the most relevant prediction context
)
print(completion.choices[0].message.content)
See the forecasting guides for how to write good forecasting prompts.
lr.predict() wraps the same API and parses the structured answer for you:
pip install lightningrod-ai
import lightningrod as lr
client = lr.LightningRod(api_key="your-api-key")
result = client.predict(
"Will the Fed cut rates by 25bp in March 2026?",
answer_type="binary",
research=True,
)
print(result.binary.probability) # e.g. 0.62
Need a model tuned to your domain? Our platform turns raw sources into labeled datasets and fine-tuned models. Read the Future-as-Label paper or view public models and datasets on Hugging Face.
npx claudepluginhub lightning-rod-labs/lightningrod-python-sdk --plugin lightningrod-python-sdkGive Claude Code a research team. Forecast, score, classify, or research every row of a dataset.
Time series forecasting and analysis
Earth2Studio skills for weather and climate AI — discover models, install the package, fetch data, run deterministic forecasts, and create new data source or prognostic model wrappers.
Data engineering and time series analysis mastery. Expert in jq, SQL, pandas, time series forecasting, ETL pipelines, streaming, and analytics visualization.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub