Agent skill · Workflow & Productivity

parsing-ccda-documents

Parses C-CDA / CCD XML clinical documents to extract human-readable section narrative plus coded entries, keyed by section LOINC codes and templateIds. Use before OpenMed processing when ingesting C-CDA R2.1 documents (CCD, Discharge Summary, H&P, Consultation Note) exported from an EHR and you need the narrative section text de-identified and analyzed. Hand section narrative to openmed.deidentify and openmed.analyze_text; XML-aware de-identification that preserves CDA markup is available via openmed.interop.cda. Trigger keywords: C-CDA, CCD, CDA, clinical document, templateId, LOINC section,

Maziyar Panahi4,851★ · 1 repos on radarProfile →
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill parsing-ccda-documents --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0
Path: skills/parsing-ccda-documents/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Parsing C-CDA / CCD Documents for OpenMed C-CDA (Consolidated Clinical Document Architecture) is the XML document standard behind Meaningful Use / ONC certification — the CCD, Discharge Summary, History & Physical, and Consultation Note you get when an EHR "exports a chart". Each document is a `ClinicalDocument` with a header (patient, authors, encounter) and a `structuredBody` of **sections**. Every section has *two* representations: a human-readable **narrative `<text>` block** and machine-readable **coded entries**. The narrative is what you feed to clinical NLP. This skill extracts it and hands it to OpenMed. ## When to use - You receive C-CDA R2.1 / CCD documents (Direct messaging, patient portal export, HIE) and want the free-text section narrative for de-id and NER. - You need to pair narrative spans with the section they came from (problems, meds, allergies, results, plan, H&P narrative). - You want XML-safe de-identification that keeps the document parseable. ## C-CDA structure in one minute ```xml <ClinicalDocument xmlns="urn:hl7-org:v3"> <recordTarget><patientRole> <id extension="12345" root="..."/> <patient><name><given>Jane</given><family>Doe</family></name> <birthTi

What's inside
Steps it walks through
  1. When to use
  2. C-CDA structure in one minute
  3. Quick start
  4. XML-aware whole-document de-identification
  5. Workflow
  6. Hand-off to / from OpenMed
  7. Edge cases & gotchas
  8. Standards & references
More from openmed
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About this skill
What does the parsing-ccda-documents skill do?

Parses C-CDA / CCD XML clinical documents to extract human-readable section narrative plus coded entries, keyed by section LOINC codes and templateIds. Use before OpenMed processing when ingesting C-CDA R2.1 documents (CCD, Discharge Summary, H&P, Consultation Note) exported from an EHR and you need the narrative section text de-identified and analyzed. Hand section narrative to openmed.deidentify and openmed.analyze_text; XML-aware de-identification that preserves CDA markup is available via openmed.interop.cda. Trigger keywords: C-CDA, CCD, CDA, clinical document, templateId, LOINC section,

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill parsing-ccda-documents --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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