---

name: "model-development" description: "Routes BasicTS built-in model selection and custom model authoring, including forward contracts, config classes, and auxiliary-loss behavior." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

model-development

Use this sub-skill when the task is about choosing a BasicTS model family, understanding a model config, or authoring a new model that BasicTS can run.

Typical triggers

• "Which BasicTS model should I use?"
• "How do I implement a custom BasicTS model?"
• "What does a BasicTS model need to return?"
• "How do auxiliary losses work?"
• "Check whether this model matches the BasicTS contract"

What this sub-skill covers

• built-in model families under src/basicts/models/
• model config classes such as DLinearConfig, iTransformerConfig, TimesNetConfig, and PatchTSTConfig
• the BasicTS forward contract for inputs, optional timestamps, masks, and training-step metadata
• output expectations for prediction tensors, dictionaries, and optional loss or auxiliary-loss keys
• CPU-side contract inspection for custom models

Read these bundled references first

• references/model-catalog.md for model families, task support, and entry-point names.
• references/custom-model-contract.md for the forward/output contract and custom model checklist.
• references/troubleshooting.md for shape, signature, and callback failures.
• scripts/check_model_contract.py for a safe inspection helper.

Route here when the user asks for

• built-in model selection for forecasting, classification, or imputation
• the difference between *_ForForecasting, *_ForClassification, and *_ForReconstruction
• how to implement a custom model class that BasicTS can train or evaluate
• how to inspect a model's accepted inputs or output structure
• how to wire auxiliary losses from the model into the BasicTS callback path

Route elsewhere when the user asks for

• dataset folders, raw arrays, or conversion scripts → data-preparation
• launcher commands, configs, or checkpoint usage → training-evaluation
• callbacks, metrics, scalers, taskflows, or config serialization beyond the model contract → pipeline-extension

Working guidance

1. Confirm the target task family first.
2. Pick a built-in model wrapper that matches the task if one exists.
3. Make sure the model's forward accepts inputs and any extra keys it needs.
4. Return a tensor or a dictionary with prediction.
5. If the model computes its own loss, document how that interacts with the BasicTS runner and callbacks.

When to read the helper script

Use scripts/check_model_contract.py when you want to inspect a model class from the installed package, print its forward signature, or run a small dummy forward check.

Why this sub-skill exists

BasicTS model usage is broader than a single model family. This sub-skill keeps the built-in catalog and the custom model contract separate from dataset and training concerns so future agents can answer model questions quickly and accurately.