nlp

name	nlp
description	Use this skill when users need Chinese or Classical Chinese NLP capabilities, including tokenization, named entity recognition, POS tagging, dependency/semantic parsing, and semantic role labeling. Suitable for information extraction, structured understanding, preprocessing, and diagnostics. Do not use for model training, fine-tuning, translation-only tasks, or requests without text input.
metadata	{"builtin_skill_version":"1.0","qwenpaw":{"emoji":"🧠","requires":{}}}
tags	["nlp","chinese","classical-chinese","ner","parsing"]
channels	["all"]

NLP Multi-Task Builtin Skill

When to use

Use this skill for:

• Chinese and Classical Chinese tokenization
• Named Entity Recognition (NER)
• POS tagging (CTB/PKU/863)
• Dependency parsing, semantic dependency, constituency parsing
• Semantic role labeling (SRL)
• Structured NLP outputs for downstream workflows

Unified API endpoint

All tasks share one endpoint:

POST /api/nlp/tasks/{task_key}/run

Minimal payload:

{
  "text": "Beijing Jiulu Technology Co., Ltd. was founded in September 2022.",
  "request_id": "nlp-skill-demo-001"
}

Supported task_key values

Modern Chinese:

• tokenize
• ner
• pos_ctb
• pos_pku
• pos_863
• dep
• sdp
• con
• srl

Classical Chinese:

• lzh_tok_fine
• lzh_tok_coarse
• lzh_lem
• lzh_pos_upos
• lzh_pos_xpos
• lzh_pos_pku
• lzh_dep

Input priority

If multiple input fields are provided, use this priority:

1. tokens_batch
2. tokens
3. texts
4. text

Constraints:

• texts / tokens_batch: max 5 items per request
• If input is empty, ask the user for text before execution

Recommended execution flow

1) Identify user intent

Clarify which output is needed:

• Tokenization vs entity extraction
• Syntactic vs semantic analysis
• Modern Chinese vs Classical Chinese

2) Pick a task_key

Choose the minimal task that satisfies the request.

3) Execute and parse

Call /api/nlp/tasks/{task_key}/run, then read:

• result (primary output)

Use advanced fields only when needed:

• status
• reason_code
• duration_ms
• resolved_model
• preload_status

4) Return actionable output

Present output in this order:

• Key result first (entities/tokens/dependencies)
• Supporting details second (latency/errors/notes)

Failure handling

Sidecar not configured

If status is not ready and reason_code indicates runtime/sidecar not ready:

• Clearly state runtime is unavailable
• Ask the user to check NLP runtime config/status
• Never fabricate NLP results

Cold start and model download

First-run latency can be high. Handle by:

• Informing the user about model initialization
• Returning actionable next steps on timeout (retry/lightweight task/check cache)

Invalid parameters

For invalid task_key or bad payload:

• Point out invalid fields clearly
• Provide a copyable corrected payload

Output layering

• Default layer: user-facing NLP result
• Advanced layer: debug metadata (status/reason_code/duration_ms)

Example:

{
  "task": "ner",
  "summary": "2 entities detected",
  "entities": [
    {"text": "北京九录科技有限公司", "label": "ORG"},
    {"text": "孟繁永", "label": "PERSON"}
  ],
  "debug": {
    "status": "ready",
    "reason_code": "HANLP_READY",
    "duration_ms": 42
  }
}

CLI examples

curl -s -X POST "http://127.0.0.1:8088/api/nlp/tasks/tokenize/run" \
  -H "Content-Type: application/json" \
  -d '{"text":"北京九录科技有限公司成立于2022年9月。","request_id":"nlp-cli-tokenize-001"}'

curl -s -X POST "http://127.0.0.1:8088/api/nlp/tasks/lzh_tok_fine/run" \
  -H "Content-Type: application/json" \
  -d '{"text":"赫赫九录，肇基京华。孟氏创立，岁在孟秋。","request_id":"nlp-cli-lzh-001"}'

Do not

• Do not fabricate model outputs
• Do not claim analysis was executed without calling the API
• Do not expose debug fields as primary user-facing result