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name: chronos-2-forecasting description: "Use Chronos2Pipeline for Chronos-2 zero-shot, covariate-aware, multivariate, and fine-tuned forecasting workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Chronos-2 forecasting router

Use this sub-skill when the task is to run or reason about Chronos2Pipeline inference or model loading for Chronos-2: zero-shot forecasts, saved or fine-tuned Chronos-2 checkpoints, tensor/list/list-of-dicts inputs, multivariate targets, covariates, predict, predict_quantiles, predict_df, predict_fev, embed, long-horizon unrolling, and model-selection tradeoffs.

Start here

• API signatures, parameters, return shapes, model properties, and loading behavior: references/api-reference.md
• Task recipes for model loading, tensor/list/list-dict inputs, DataFrame covariates, long horizons, embeddings, fev bridge, and save/load: references/workflows.md
• Symptoms and recovery for schema, covariate, future horizon, frequency, cache/download, and device/dtype issues: references/troubleshooting.md
• Safe helper script with no default model download: scripts/chronos2_smoke_forecast.py

Route out of this sub-skill

• Detailed DataFrame, future covariate, categorical, timestamp, and preprocessing validation rules belong in data-formats-and-validation.
• Chronos-Bolt and original Chronos/T5 pipelines belong in chronos-bolt-and-original.
• Full fine-tuning, evaluation benchmarks, aggregate scoring, and deployment operations belong in training-evaluation-deployment.

Operating guardrails

• Prefer BaseChronosPipeline.from_pretrained(...) for general loading and confirm that the returned object is Chronos2Pipeline before using Chronos-2-specific covariate or multivariate behavior.
• Do not trigger Hugging Face, S3, benchmark, or cloud downloads unless the user explicitly provides a model/dataset/URI and asks to load or evaluate it.
• Keep prediction_length, context_length, batch_size, device_map, and dtype explicit in reproducible snippets; inspect loaded model properties instead of hard-coding model limits.
• Use sibling validation guidance before disabling validate_inputs or building heterogeneous list-of-dicts inputs.