segmenting-clinical-sections
segmenting-clinical-sections is an NLP, speech & audio ML skill for AI agents, published by maziyarpanahi in the openmed repository on GitHub. Skillspace rates segmenting-clinical-sections C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Split a clinical note into canonical sections (Chief Complaint, HPI, PMH, Medications, Allergies, Assessment & Plan, etc.) before running OpenMed NER or de-identification, so section context sharpens downstream precision. Use when the user has a free-text note or discharge summary and wants section-aware processing, header detection, mapping headers to LOINC document-section codes, or per-section NER/de-id. Covers heuristic header detection, normalization to canonical section labels, LOINC/SecTag framing, and why a finding in PMH is historical while the same finding in A&P is active. Hand-off: feed each sectioned chunk into openmed.analyze_text / openmed.deidentify. Pairs before extracting-clinical-entities.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 4,654
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Section segmentation feeding OpenMed's public NER models
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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