# Section Detection

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

- Skill: `majiayu000/section-detection` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/section-detection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/section-detection/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/section-detection

---


# 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`.