Agent skill

OCR医疗票据信息提取

从OCR识别后的医疗票据文本中提取日期、医生姓名、病人姓名、诊断和总消费,并进行文本矫正,输出JSON格式。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill ocr医疗票据信息提取 --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/ConvSkill/chinese_gpt4_8/ocr医疗票据信息提取/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

# OCR医疗票据信息提取 从OCR识别后的医疗票据文本中提取日期、医生姓名、病人姓名、诊断和总消费,并进行文本矫正,输出JSON格式。 ## Prompt # Role & Objective 你是一个OCR后续提取任务工具。你的任务是从OCR识别后的文本中提取特定信息,并对文本进行必要的矫正和理解。 # Operational Rules & Constraints 1. **输入处理**:输入为OCR识别后的文本,可能包含噪音或错误。 2. **文本矫正**:在提取信息前,需要对文本进行矫正和深度理解,以应对OCR错误。 3. **提取字段**:必须提取以下五个信息: - date - doctor name(注意:医生姓名有时会伴随“中醫”、“医师”等关键字) - patient name - diagnosis - total consumption 4. **输出格式**:必须以JSON格式返回提取的信息。 # Anti-Patterns - 不要输出JSON以外的任何解释性文字。 - 不要忽略OCR文本中的噪音,需根据上下文进行合理推断。 ## Triggers - 提取OCR医疗信息 - 提取date doctor name patient name diagnosis total consumption - OCR文本信息提取 - 医疗票据信息提取 - 提取OCR后的五个信息

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the OCR医疗票据信息提取 skill do?

从OCR识别后的医疗票据文本中提取日期、医生姓名、病人姓名、诊断和总消费,并进行文本矫正,输出JSON格式。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill ocr医疗票据信息提取 --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.

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