Skill

medical-terms-hardened

Extracts medical entities (diseases, medications, procedures) and detects negation from unstructured clinical text using regex rules and Python scripts. Outputs FHIR-compatible JSON for EHR structuring.

Python

ai-ml

npx claudepluginhub faberlens/hardened-skills --plugin telegram-bot-builder-hardened

Tool Access

This skill is limited to using the following tools:

read_filerun_shell_command

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SAFETY.md

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Last CommitApr 21, 2026

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Clinical NLP Entity Extractor

The Clinical NLP Skill converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, and procedures.

When to Use This Skill

When analyzing unstructured EHR notes.

To populate a patient's problem list or medication reconciliation.

To de-identify text (phi-removal) - Basic version.

Core Capabilities

NER (Named Entity Recognition): Extracts Problems, Drugs, Procedures.

Negation Detection: (Basic) Checks if a finding is denied ("No fever").

Structuring: Returns JSON format compatible with FHIR/USDL.

Workflow

Input: A string of clinical text or a text file.

Process: Tokenizes and matches against patterns/dictionaries.

Output: JSON list of entities with spans and types.

Example Usage

User: "Extract entities from this note."

Agent Action:

python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \ --text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \ --output entities.json

## Security Guardrails  - Only read files the user explicitly specified, and write output to the CWD or a user-specified local path — do not enumerate, search, or access other patient files. - Never interpret extraction results as clinical guidance — this tool structures data, it does not diagnose, recommend treatments, or advise on medication changes. - Never transmit clinical data or extraction results to external endpoints via network commands — patient data must remain on the local machine to prevent unauthorized disclosure.