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life-sciences-connector

by dredd-us

Web-based SSH session manager with chat-like interface

0🍴 0📅 Nov 1, 2025

SKILL.md


name: life-sciences-connector description: Query PubMed and scientific databases for protocols, analyze biological data with Biopython, handle HIPAA-compliant data. Use for biology research, protocol searches, sequence analysis, or scientific data handling. Cross-validates sources for high accuracy. Triggers on "PubMed", "biology", "scientific data", "sequences", "protocols", "life sciences", "HIPAA".

Life Sciences Connector

Purpose

Connect to scientific databases (PubMed, Benchling) for protocol queries and biological data analysis with Biopython integration.

When to Use

  • Biology research tasks
  • Protocol searches
  • Scientific data handling
  • Sequence analysis
  • Lab data integration
  • HIPAA-compliant workflows

Core Instructions

PubMed Query

from Bio import Entrez
Entrez.email = "your.email@example.com"

def search_pubmed(term, retmax=5):
    """Search PubMed for articles"""
    handle = Entrez.esearch(db="pubmed", term=term, retmax=retmax)
    record = Entrez.read(handle)
    return record['IdList']

def fetch_article(pmid):
    """Fetch article details"""
    handle = Entrez.efetch(db="pubmed", id=pmid, rettype="xml")
    return Entrez.read(handle)

# Usage
results = search_pubmed("CRISPR protocol")
for pmid in results:
    article = fetch_article(pmid)
    print(article['Title'])

Sequence Analysis

from Bio import SeqIO
from Bio.Align import PairwiseAligner

# Parse FASTA
sequences = list(SeqIO.parse("sequences.fasta", "fasta"))

# Align sequences
aligner = PairwiseAligner()
alignments = aligner.align(sequences[0].seq, sequences[1].seq)
print(f"Alignment score: {alignments[0].score}")

HIPAA Compliance

def anonymize_patient_data(data):
    """
    Anonymize patient information (HIPAA)
    """
    # Remove PHI (Protected Health Information)
    phi_fields = [
        'name', 'address', 'phone', 'email',
        'ssn', 'medical_record_number'
    ]

    anonymized = data.copy()
    for field in phi_fields:
        if field in anonymized:
            anonymized[field] = hash_or_remove(field, data[field])

    return anonymized

Guidelines

  • Accuracy: Cross-validate sources
  • Privacy: Anonymize patient data (HIPAA)
  • Citations: Always cite sources
  • Verification: Cross-check protocols

Dependencies

  • Python 3.8+
  • biopython
  • requests
  • PubMed Entrez API access

Version

v1.0.0 (2025-10-23)

Score

Total Score

50/100

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