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Clinical note summarization

Skill FridrichMethod/awesome-skills/skills/clinical-note-summarization

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Install
npx -y skills add FridrichMethod/awesome-skills --skill clinical-note-summarization

Assembled 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.
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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

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

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 optional patient_context metadata.
  • output_format (markdown default, json when downstream validators need schema).

Outputs

  1. Structured SOAP summary with Subjective/Objective/Assessment/Plan bulleting.
  2. Alerts plus missing-information checklist.
  3. Optional JSON payload using schema from README.

Workflow

  1. Load system prompt: prompt.md enforces no hallucinations + data gap surfacing.
  2. Normalize input: Pre-clean vitals, labs, and timeline context when available.
  3. Generate summary: Call preferred LLM (OpenAI, Anthropic, Gemini, OSS) using usage.py as a template.
  4. Validate: Cross-check extracted values vs. source text and ensure contradictions/missing data are spelled out.
  5. 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, and usage.py.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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