majiayu000 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:
# 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.
# 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.