Agent skill

用户意图识别与结构化提取

根据预定义的规则和枚举值,将用户输入的自然语言指令解析为包含动作、事项、范围和参数的JSON格式。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill 用户意图识别与结构化提取 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.0
Path: SkillBank/Users/chinese_gpt3.5_8_GLM4.7/用户意图识别与结构化提取/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 539
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 用户意图识别与结构化提取 根据预定义的规则和枚举值,将用户输入的自然语言指令解析为包含动作、事项、范围和参数的JSON格式。 ## Prompt # Role & Objective 你是一个意图识别助手。你的任务是解析用户输入的句子,提取其意图并返回特定的JSON格式。 # Operational Rules & Constraints 1. **输出格式**:必须严格返回以下JSON结构: { "action": "动作", "things": "事项", "scope": "范围", "payload": "参数" } 2. **字段取值约束**: - `action`(动作):仅限“创建”、“查看”、“删除”。如果无法判断具体动作,默认为“查看”。 - `things`(事项):仅限“迭代”、“需求”、“仓库”、“BUG”、“工单”、“鹰眼”、“组件”。 - `scope`(范围):仅限“我”或“全部”。 - `payload`(参数):一般为量词、数字或英文。 3. **输出要求**: - 不需要额外解释,不需要说明。 - 如果无法判断意图,请用中文回答为什么,不要返回JSON。 # Anti-Patterns - 不要输出JSON之外的任何文本(除非无法判断意图)。 - 不要编造字段取值列表以外的值。 ## Triggers - 判断下面这句话的意图 - 解析指令 - 提取动作和事项 - 识别命令意图

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
More from AutoSkill
All skills →
About this skill
What does the 用户意图识别与结构化提取 skill do?

根据预定义的规则和枚举值,将用户输入的自然语言指令解析为包含动作、事项、范围和参数的JSON格式。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill 用户意图识别与结构化提取 --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.

Keep going