Menu
当用户需要对中文或古汉语文本做分词、实体识别、词性标注、依存/语义分析、语义角色分析时，使用此技能。适用于信息抽取、结构化理解、文本预处理、规则验证与故障排查场景。不用于模型训练、微调、跨语言翻译或无文本输入的任务。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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.
Comprehensive guide for CoPaw pipeline JSON schema, step definitions, validation rules, and composition best practices. Use when creating, editing, or discussing workflow orchestration templates. Fill in the Quick Start context card on first turn to generate a pipeline JSON draft without multi-round clarification.
Use this skill when another agent's expertise/context is needed, or when the user explicitly asks to involve another agent. First list agents, then use qwenpaw agents chat for two-way communication with replies. | 当需要其他 agent 的专长/上下文，或用户明确要求调用其他 agent 时使用；先查 agent，再用 qwenpaw agents chat 双向通信（有回复）
当需要其他 agent 的专长、上下文或协作支持，或用户明确要求调用其他 agent 时，使用本 skill。先查询可用 agents，再用 qwenpaw agents chat 进行双向沟通。
Enforce project path resolution and four-artifact governance for this project workspace.
Use knowledge_search proactively when the user is asking about existing project facts, process notes, prior decisions, archived materials, or whether the knowledge base already contains something relevant.
| name | nlp |
| description | 当用户需要对中文或古汉语文本做分词、实体识别、词性标注、依存/语义分析、语义角色分析时，使用此技能。适用于信息抽取、结构化理解、文本预处理、规则验证与故障排查场景。不用于模型训练、微调、跨语言翻译或无文本输入的任务。 |
| metadata | {"builtin_skill_version":"1.0","qwenpaw":{"emoji":"🧠","requires":{}}} |
| tags | ["nlp","chinese","classical-chinese","ner","parsing"] |
| channels | ["all"] |
当用户请求以下能力时使用本技能：
所有任务统一调用：
POST /api/nlp/tasks/{task_key}/run
示例最小请求体：
{
"text": "北京九录科技有限公司成立于2022年9月。",
"request_id": "nlp-skill-demo-001"
}
现代中文：
古汉语：
如果同时提供多个输入字段，按以下优先级选择：
约束：
先确认用户要的结果类型：
按目标选择最小必要任务，不做无关任务叠加。
调用 /api/nlp/tasks/{task_key}/run 后，优先读取：
必要时查看高级信息：
输出应先给结论，再给证据：
若 status 非 ready 且 reason_code 指向 sidecar/环境未就绪：
首次调用可能耗时较长。处理原则：
若 task_key 不支持或输入不合法：
示例（简化）：
{
"task": "ner",
"summary": "识别到 2 个实体",
"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
}
}
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"}'