Clinical note summarization
Skill FridrichMethod/awesome-skills/skills/clinical-note-summarization
Curated, auto-synced collection of 2,000+ Claude Code & Codex skills for AI4Protein, bioinformatics, AI development, and academic paper writing. One curl command installs them all.
npx -y skills add FridrichMethod/awesome-skills --skill clinical-note-summarizationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Structure raw clinical notes into SOAP-format summaries with explicit contradictions, missing data, and ICD-linked assessments using the provided prompt + usage script.
SKILL.md
2.1 KB, as published. Nobody here has run it
At-a-Glance
- description (10-20 chars): SOAP builder
- keywords: clinical-notes, SOAP, guardrails, ICD10, gaps
- measurable_outcome: Produce SOAP markdown + JSON (when requested) covering all four sections with ≥95% note coverage and explicit missing info in ≤2 minutes per note.
Inputs
note_text(dictation, OCR, or EHR export) and optionalpatient_contextmetadata.output_format(markdowndefault,jsonwhen downstream validators need schema).
Outputs
- Structured SOAP summary with Subjective/Objective/Assessment/Plan bulleting.
- Alerts plus missing-information checklist.
- Optional JSON payload using schema from README.
Workflow
- Load system prompt:
prompt.mdenforces no hallucinations + data gap surfacing. - Normalize input: Pre-clean vitals, labs, and timeline context when available.
- Generate summary: Call preferred LLM (OpenAI, Anthropic, Gemini, OSS) using
usage.pyas a template. - Validate: Cross-check extracted values vs. source text and ensure contradictions/missing data are spelled out.
- Deliver output: Provide markdown + JSON as required and log PHI handling steps.
Guardrails
- Never invent findings; state "not provided" explicitly.
- Mark outputs as documentation support only—not clinical decisions.
- Strip/re-mask PHI before storing prompts/responses.
References
- For detailed schema, guardrails, and integration snippets see
README.md,prompt.md, andusage.py.