---

name: "pipeline-extension" description: "Routes BasicTS callback, metric, scaler, taskflow, and config-extension workflows that customize the training pipeline." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

pipeline-extension

Use this sub-skill when the task is about changing how BasicTS trains, validates, masks, scales, measures, or dispatches data through the runner pipeline.

Typical triggers

• "Add a callback to BasicTS"
• "How do I use auxiliary losses?"
• "Write a custom metric or scaler"
• "Customize the forecasting taskflow"
• "What do the config shortcuts do?"

What this sub-skill covers

• BasicTSRunner
• BasicTSTaskFlow and the task-specific taskflows
• BasicTSCallback and the built-in callback catalog
• metrics, meters, and loss-handling behavior
• ZScoreScaler and MinMaxScaler
• config shortcuts, checkpoint settings, and serialization behavior

Read these bundled references first

• references/configuration.md for config packing, shortcut fields, and checkpoint defaults.
• references/taskflow-and-callbacks.md for hook order, taskflow behavior, and metric wiring.
• references/troubleshooting.md for callback, metric, scaler, and config failures.
• scripts/inspect_pipeline_contract.py for a safe inspection helper.

Route here when the user asks for

• custom callbacks or callback ordering
• auxiliary-loss wiring
• metric functions or metric selection
• scaler behavior or inverse transforms
• taskflow preprocessing/postprocessing/masking
• config shortcut behavior or checkpoint save strategy

Route elsewhere when the user asks for

• dataset file formats or raw conversion → data-preparation
• built-in model selection or custom model forward rules → model-development
• launcher commands, training entry points, or checkpoint evaluation → training-evaluation

Working guidance

1. Identify whether the user wants a hook, a metric, a scaler, or a taskflow change.
2. Keep the runner contract in mind: the taskflow preprocesses data before the forward pass and postprocesses it before metrics.
3. Match metric signatures to the keys that the runner returns.
4. Attach AddAuxiliaryLoss when the model emits named auxiliary losses.
5. Check ckpt_save_strategy, val_interval, and test_interval together when debugging save behavior.

When to read the helper script

Run scripts/inspect_pipeline_contract.py when you want a quick read-only summary of the installed BasicTS runner/taskflow/callback/metric/scaler contract or a tiny synthetic validation of the masking and metric helpers.

Why this sub-skill exists

BasicTS is highly configurable, and a small hook or metric change often affects multiple parts of the pipeline. This sub-skill keeps those extension points together so future agents can reason about the runtime behavior without opening the source repository again.