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checkup-report-interpreter

Interprets physical examination reports by parsing PDF files into structured data, identifying abnormalities with severity grading, and generating clinical explanations with health recommendations. Supports year-over-year comparison of two reports. Use when the user uploads a checkup report or asks for help understanding lab results.

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概览

安装命令

npx skills add https://github.com/vitaclaw/vitaclaw --skill checkup-report-interpreter

复制此命令并粘贴到 Claude Code 中以安装该技能

来源

vitaclaw/vitaclaw

星标17

分支3

更新时间2026年3月23日 16:00

文件资源管理器

3 个文件

SKILL.md

readonly

name	checkup-report-interpreter
description	Interprets physical examination reports by parsing PDF files into structured data, identifying abnormalities with severity grading, and generating clinical explanations with health recommendations. Supports year-over-year comparison of two reports. Use when the user uploads a checkup report or asks for help understanding lab results.
version	1.0.0
user-invocable	true
argument-hint	[report <pdf> \| parse <pdf> \| extract <pdf> \| compare <pdf1> <pdf2>]
allowed-tools	Read, Grep, Glob, Write, Edit
metadata	{"openclaw":{"emoji":"🏥","category":"health-scenario"}}

Checkup Report Interpreter

Automatically parses physical examination report PDFs into structured data, then uses LLM for abnormality identification, clinical interpretation, and health recommendation generation.

Features

Smart PDF Parsing: Uses PyMuPDF to extract text from checkup reports, supporting multi-page reports
Structured Extraction: LLM identifies all examination items (laboratory tests, imaging findings, physical examination), returning standardized JSON
Abnormality Grading: Automatically determines abnormality severity based on reference ranges (urgent / important / moderate / minor), with 40+ built-in common indicator reference values
Clinical Interpretation: Groups abnormal indicators by organ system and explains their clinical significance in plain language
Health Recommendations: Generates personalized health recommendations and suggested follow-up items
Annual Comparison: Supports item-by-item comparison of two reports, highlighting new abnormalities, worsening trends, and improvements

Usage

# Full report interpretation
python checkup_report_interpreter.py report checkup_report.pdf

# Parse PDF text only
python checkup_report_interpreter.py parse checkup_report.pdf

# Extract structured examination items
python checkup_report_interpreter.py extract checkup_report.pdf

# Compare two years of reports
python checkup_report_interpreter.py compare 2026_checkup.pdf 2025_checkup.pdf

Environment Variables

Variable	Required	Description
`OPENROUTER_API_KEY`	Yes	OpenRouter API key
`OPENROUTER_BASE_URL`	No	API endpoint (default: `https://openrouter.ai/api/v1/chat/completions`)
`LLM_MODEL`	No	Model name (default: `google/gemini-2.5-flash`)

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2026-03-2317

来源

vitaclaw

vitaclaw/vitaclaw

打开 GitHub 仓库查看创作者相关仓库

安装命令

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在 Manus 中运行

适用职业SOC

软件开发工程师计算机与数学类职业15-1252L4

name	checkup-report-interpreter
description	Interprets physical examination reports by parsing PDF files into structured data, identifying abnormalities with severity grading, and generating clinical explanations with health recommendations. Supports year-over-year comparison of two reports. Use when the user uploads a checkup report or asks for help understanding lab results.
version	1.0.0
user-invocable	true
argument-hint	[report <pdf> \| parse <pdf> \| extract <pdf> \| compare <pdf1> <pdf2>]
allowed-tools	Read, Grep, Glob, Write, Edit
metadata	{"openclaw":{"emoji":"🏥","category":"health-scenario"}}

Checkup Report Interpreter

Automatically parses physical examination report PDFs into structured data, then uses LLM for abnormality identification, clinical interpretation, and health recommendation generation.

Features

Smart PDF Parsing: Uses PyMuPDF to extract text from checkup reports, supporting multi-page reports
Structured Extraction: LLM identifies all examination items (laboratory tests, imaging findings, physical examination), returning standardized JSON
Abnormality Grading: Automatically determines abnormality severity based on reference ranges (urgent / important / moderate / minor), with 40+ built-in common indicator reference values
Clinical Interpretation: Groups abnormal indicators by organ system and explains their clinical significance in plain language
Health Recommendations: Generates personalized health recommendations and suggested follow-up items
Annual Comparison: Supports item-by-item comparison of two reports, highlighting new abnormalities, worsening trends, and improvements

Usage

# Full report interpretation
python checkup_report_interpreter.py report checkup_report.pdf

# Parse PDF text only
python checkup_report_interpreter.py parse checkup_report.pdf

# Extract structured examination items
python checkup_report_interpreter.py extract checkup_report.pdf

# Compare two years of reports
python checkup_report_interpreter.py compare 2026_checkup.pdf 2025_checkup.pdf

Environment Variables

Variable	Required	Description
`OPENROUTER_API_KEY`	Yes	OpenRouter API key
`OPENROUTER_BASE_URL`	No	API endpoint (default: `https://openrouter.ai/api/v1/chat/completions`)
`LLM_MODEL`	No	Model name (default: `google/gemini-2.5-flash`)