---

name: training-and-development description: "Guides HanLP training, fine-tuning, editable source installs, focused tests, optional backend choices, and safe inspection of training APIs without launching expensive jobs by default." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training And Development

Use this sub-skill when the user wants to train or fine-tune HanLP components, inspect fit/evaluate APIs, maintain a source checkout, run focused tests, choose optional extras, or adapt training demos safely.

Read First

• Read references/training-recipes.md for source-backed tokenizer/classifier training recipes and safety boundaries.
• Read references/development-guide.md for editable install order, CI-style tests, and maintainer workflow guidance.
• Read references/troubleshooting.md for dataset/cache/device/training failures.
• Run scripts/inspect_training_api.py --json to inspect training signatures without downloads or training.

Minimal Maintainer Setup

python -m pip install -e plugins/hanlp_trie
python -m pip install -e plugins/hanlp_common
python -m pip install -e plugins/hanlp_restful
python -m pip install -e .
python -m pip install pytest

Run bundled source-free helper checks for the affected area from the generated skill root before any expensive validation:

python scripts/check_hanlp_environment.py --json
python sub-skills/rules-and-trie/scripts/rules_smoke.py
python sub-skills/rules-and-trie/scripts/trie_smoke.py
python sub-skills/native-workflows/scripts/pipeline_smoke.py
python sub-skills/training-and-development/scripts/inspect_training_api.py --json

For source-maintainer work, choose private native tests from the checkout only after deciding the edited area and avoiding live RESTful, model-download, or training-scale jobs by default. Training scripts may download datasets/models, write model artifacts, and require GPU/time.