---

name: "basic-ts" description: "Routes BasicTS time-series training, dataset, model, and pipeline workflows through focused sub-skills." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

BasicTS

BasicTS is a time-series analysis toolkit and benchmark library. Use this root skill as the router for the package's main user-facing workflows.

Install and inspect

A simple public install path is:

pip install basicts

For local development against a checkout, editable install is also fine:

pip install -e .

Minimal import check:

python -I -c "import basicts; print(basicts.__version__)"

For a friendlier inspection summary, run scripts/check_basic_ts_install.py.

Route map

User request	Read first
Train, evaluate, resume, or quick-start a BasicTS run	sub-skills/training-evaluation/SKILL.md
Inspect a built-in model, author a custom model, or check forward contracts	sub-skills/model-development/SKILL.md
Validate a dataset folder, raw conversion, or tiny fixture layout	sub-skills/data-preparation/SKILL.md
Customize callbacks, metrics, scalers, taskflows, or config behavior	sub-skills/pipeline-extension/SKILL.md

When to read the bundled references

• Read references/repo-provenance.md when you need to check whether this skill still matches the current BasicTS checkout.
• Read references/troubleshooting.md when imports, datasets, configs, callbacks, or checkpoints fail in a cross-cutting way.
• Read references/repo-routing-metadata.json when you need router placement details for import or selection logic.

What this root skill does not cover

• It does not replace the repository's source code.
• It does not include the optional web/server surface as a first-class route.
• It does not include release or packaging-maintainer workflows.

Root guidance

1. Start with the route map above.
2. Pick the narrowest sub-skill that matches the user's request.
3. Use the root troubleshooting reference only for cross-cutting installation, import, dataset-path, or checkpoint issues.
4. Check provenance before treating the skill as current for a different checkout.

Quick signals

• BasicTSLauncher and checkpoint questions → training-evaluation
• forward, model output keys, or auxiliary loss → model-development
• train_data.npy, train_inputs.npy, shape.npy, or raw dataset conversion → data-preparation
• callbacks, metrics, scalers, taskflow, or config shortcuts → pipeline-extension

How to use the install check script

Run scripts/check_basic_ts_install.py when you want a read-only summary of the installed package, launcher signature, and core import surface.