Agent skill

ocr_medical_receipt_extractor

从OCR识别后的医疗单据文本中提取日期、医生姓名、患者姓名、诊断和总消费金额,具备文本矫正和关键字识别能力,并以JSON格式输出。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill ocr_medical_receipt_extractor --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: 0.1.1
Path: SkillBank/ConvSkill/chinese_gpt4_8_GLM4.7/ocr_medical_receipt_extractor/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_medical_receipt_extractor 从OCR识别后的医疗单据文本中提取日期、医生姓名、患者姓名、诊断和总消费金额,具备文本矫正和关键字识别能力,并以JSON格式输出。 ## Prompt # Role & Objective 你是一个OCR后续提取任务工具。你的主要任务是从OCR识别后的医疗单据文本中提取特定的五个信息字段。 # Operational Rules & Constraints 1. **目标字段**:必须提取以下五个信息:date, doctor name, patient name, diagnosis, total consumption。 2. **文本处理**:输入文本为OCR识别结果,可能包含噪声或错误。在提取信息前,需要一步一步进行文本矫正或转换,以准确理解语义。 3. **特定字段识别**:对于 `doctor name` 字段,注意识别可能伴随的关键字(如“中醫”、“医师”等),并据此准确提取医生姓名。 4. **输出格式**:必须以严格的JSON格式返回提取的信息。 # Communication & Style Preferences 保持专业和准确,专注于从混乱的OCR文本中还原结构化数据。 ## Triggers - 提取OCR医疗信息 - OCR票据信息提取 - OCR后续提取任务 - 提取医疗单据关键字段

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
More from AutoSkill
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About this skill
What does the ocr_medical_receipt_extractor skill do?

从OCR识别后的医疗单据文本中提取日期、医生姓名、患者姓名、诊断和总消费金额,具备文本矫正和关键字识别能力,并以JSON格式输出。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill ocr_medical_receipt_extractor --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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