---

name: hanlp description: "Routes agents using HanLP for multilingual NLP via native Python models, RESTful clients, Document outputs, custom dictionaries, and training or repository-maintenance workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

HanLP

Use this repo skill when a task involves HanLP: multilingual Chinese/English/Japanese NLP, RESTful parsing, native Python pretrained models, task keys such as tok, pos, ner, dep, srl, sdp, con, amr, the hanlp_common.Document output format, trie/custom dictionary behavior, or maintaining/training HanLP components.

HanLP has two main use modes:

• RESTful API: install hanlp-restful, create HanLPClient, and call a remote service. This is lightweight and convenient, but needs a reachable service and may need auth.
• Native Python API: install hanlp, load pretrained components with hanlp.load, or compose hanlp.pipeline. This runs locally, may download model archives into HANLP_HOME, and can use CPU or GPU depending on the installed backend and devices settings.

Start Here

• Read references/installation-and-configuration.md when choosing install variants, cache paths, mirrors, GPU visibility, or the minimal import check.
• Read references/troubleshooting.md first for install/import, model download, GPU, RESTful service, or task-key errors.
• Read references/repo-provenance.md before deciding whether this skill matches a current checkout or should be refreshed.
• Run scripts/check_hanlp_environment.py for a source-free import, version, optional backend, and environment-variable diagnostic.

Install and Minimal Check

For RESTful client use:

python -m pip install hanlp-restful
python -c "from hanlp_restful import HanLPClient; print(HanLPClient)"

For native local models:

python -m pip install hanlp
python -c "import hanlp; print(hanlp.__version__); print(len(hanlp.pretrained.ALL))"

Optional native extras are separate: hanlp[tf] for TensorFlow/fastText paths, hanlp[amr] for AMR dependencies, and hanlp[full] for all optional groups. Do not install broad extras unless the requested workflow needs them.

Route by Task

User task	Read
Load local pretrained models, pick model identifiers, use hanlp.load, set tasks/skip_tasks, choose CPU/GPU devices, split sentences, or compose a hanlp.pipeline	sub-skills/native-workflows/SKILL.md
Use the hosted or self-hosted RESTful API from Python, Java, or Go; build parse payloads; handle auth, language, timeout, verify, or HTTP errors	sub-skills/restful-clients/SKILL.md
Interpret or validate HanLP inputs and outputs: Document, JSON task keys, token nesting, pretty_print, to_conll, span formats, or annotation labels	sub-skills/document-and-data/SKILL.md
Use deterministic sentence rules, Trie, TrieDict, custom tokenization dictionaries, dict_force, dict_combine, or POS/NER dictionary overlays	sub-skills/rules-and-trie/SKILL.md
Train/fine-tune components, inspect fit/evaluate APIs, install an editable checkout, run focused tests, or maintain the repository	sub-skills/training-and-development/SKILL.md

Common Routing Decisions

• If the user only needs quick parsing in an application and can call a service, start with restful-clients.
• If the user needs local execution, offline inference, model customization, or direct PyTorch/TensorFlow control, start with native-workflows.
• If the blocker is confusing output shape or task names such as tok/fine, ner/msra, or dep, start with document-and-data even when the output came from RESTful or native APIs.
• If the task mentions user dictionaries, gazetteers, longest-prefix matching, forced tokens, or matching spans without loading a model, start with rules-and-trie.
• If the task involves save_dir, datasets, fit, evaluate, CI tests, editable installs, or source changes, start with training-and-development.

Bundled Helper Scripts

All helpers are safe by default and do not call the RESTful service, download pretrained models, train models, or mutate external state.

• scripts/check_hanlp_environment.py: package import, distribution, environment variable, and CPU/CUDA visibility diagnostics.
• sub-skills/native-workflows/scripts/pipeline_smoke.py: no-download pipeline append/copy smoke test.
• sub-skills/native-workflows/scripts/split_sentence_smoke.py: rule-based sentence splitting smoke test.
• sub-skills/restful-clients/scripts/restful_payload_preview.py: build and validate a local /parse JSON payload without network calls.
• sub-skills/document-and-data/scripts/document_smoke.py: tiny Document JSON/CoNLL/pretty/prefix behavior check.
• sub-skills/document-and-data/scripts/validate_document_json.py: lightweight HanLP-like JSON shape validator.
• sub-skills/rules-and-trie/scripts/rules_smoke.py: rule and string utility smoke checks.
• sub-skills/rules-and-trie/scripts/trie_smoke.py: trie and dictionary smoke checks.
• sub-skills/training-and-development/scripts/inspect_training_api.py: safe training API signature inspection.

What This Skill Does Not Cover

• It does not prove live RESTful service availability, auth quota, or network conditions.
• It does not prove GPU acceleration unless the active environment has a GPU-enabled framework and a task explicitly verifies it.
• It does not run pretrained model downloads or long training jobs by default.
• It does not require the original HanLP source checkout for runtime use; source files, demos, and tests were distilled into bundled references and scripts.