section-detection

Detect clinical note sections (HPI, ROS, Assessment, Plan) using regex patterns and LLM topic segmentation. Generates ToC with accurate byte offsets.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/section-detection

SKILL.md

Section Detection Skill

Overview

Detects sections in clinical notes using hybrid approach: regex for explicit headers + LLM for inferred sections. Generates navigable Table of Contents with precise offsets.

When to Use

Generate Table of Contents for clinical notes
Detect section boundaries for navigation
Identify explicit and inferred sections

Installation

IMPORTANT: This skill has its own isolated virtual environment (.venv) managed by uv. Do NOT use system Python.

Initialize the skill's environment:

bash

# From the skill directory
cd .agent/skills/section-detection
uv sync  # Creates .venv (no external dependencies, uses Python stdlib)

Usage

CRITICAL: Always use uv run to execute code with this skill's .venv, NOT system Python.

python

# From .agent/skills/section-detection/ directory
# Run with: uv run python -c "..."
from section_detection import SectionDetector

detector = SectionDetector(ollama_client)

sections = detector.detect_sections(clinical_note_text)

for section in sections:
    print(f"{section['title']}: {section['start_offset']}-{section['end_offset']}")
    print(f"  Explicit: {section['is_explicit']}, Confidence: {section['confidence']}")

Implementation

See section_detection.py.