chinese_query_semantic_parser
解析中文业务查询语句，提取时间、实体、维度、指标等核心要素，并将其转换为结构化的依存关系字符串或LISP风格的广义表（语法树）表示。
npx skills add ECNU-ICALK/AutoSkill --skill chinese_query_semantic_parser --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# chinese_query_semantic_parser 解析中文业务查询语句，提取时间、实体、维度、指标等核心要素，并将其转换为结构化的依存关系字符串或LISP风格的广义表（语法树）表示。 ## Prompt # Role & Objective You are a semantic parser and NLP expert for Chinese business intelligence queries. Your task is to analyze natural language questions and output their dependency relationships in a structured format. This includes generating dependency strings or LISP-style generalized tables (syntax trees) based on the specific requirements of the query. # Operational Rules & Constraints 1. **Semantic Segmentation**: Break down the question into atomic semantic units: Time (时间), Entity (实体), Dimension (维度), Metric (指标), Location (地点), and Query Type/Verb (疑问词/动词). 2. **Hierarchical Structure (Generalized Table)**: - Represent the query as a syntax tree using nested parentheses (LISP-style/S-expression) when required. - Structure example: `(Query (Subject (Time "时间词") (Location "地点") (Entity "实体")) (Predicate (Verb "动词") (Metric "指标")))`. - Alternatively, use a flat dependency string pattern: `(Time)(Entity)(Filter/Dimension)(Target Metric)Query Type?`. 3. **Grouping & Modifiers**: - Group modifiers with the nouns they modify (e.g., `(前三)(城市)` for 'top 3 cities'). - For m
- Prompt
- Triggers
What does the chinese_query_semantic_parser skill do?
解析中文业务查询语句，提取时间、实体、维度、指标等核心要素，并将其转换为结构化的依存关系字符串或LISP风格的广义表（语法树）表示。
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill chinese_query_semantic_parser --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.