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Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. Triggers on "pubmed", "papers", "literature", "publications", "research on", "studies about".
---
name: pubmed-search-7ca93281c99d
description: Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. Triggers on "pubmed", "papers", "literature", "publications", "research on", "studies about".
---
# PubMed Search
Search NCBI PubMed for scientific literature using BioPython's Entrez module.
## When to Use
- User asks to find papers on a topic
- User wants recent publications in a field
- User asks for references or citations
- User wants to know the state of research on a topic
## How to Execute
### 1. Set up Entrez
```python
from Bio import Entrez
Entrez.email = "bioclaw@example.com"
```
### 2. Search PubMed
```python
# Search
handle = Entrez.esearch(db="pubmed", term="CRISPR delivery methods", retmax=20, sort="date")
record = Entrez.read(handle)
handle.close()
id_list = record["IdList"]
print(f"Found {record['Count']} results, showing top {len(id_list)}")
```
### 3. Fetch article details
```python
# Fetch details
handle = Entrez.efetch(db="pubmed", id=id_list, rettype="xml")
records = Entrez.read(handle)
handle.close()
for article in records['PubmedArticle']:
medline = article['MedlineCitation']
pmid = str(medline['PMID'])
title = medline['Article']['ArticleTitle']
# Get authors
authors = medline['Article'].get('AuthorList', [])
first_author = f"{authors[0].get('LastName', '')} {authors[0].get('Initials', '')}" if authors else "Unknown"
# Get journal and year
journal = medline['Article']['Journal']['Title']
pub_date = medline['Article']['Journal']['JournalIssue'].get('PubDate', {})
year = pub_date.get('Year', 'N/A')
# Get abstract
abstract_parts = medline['Article'].get('Abstract', {}).get('AbstractText', [])
abstract = ' '.join(str(a) for a in abstract_parts)[:300]
print(f"PMID: {pmid}")
print(f"Title: {title}")
print(f"Authors: {first_author} et al.")
print(f"Journal: {journal} ({year})")
print(f"Abstract: {abstract}...")
print(f"Link: https://pubmed.ncbi.nlm.nih.gov/{pmid}/")
print()
```
### 4. Output format for WhatsApp
```
*PubMed Search: "CRISPR delivery methods"*
_Found 1,234 results. Top 5:_
*1.* Lipid nanoparticle-mediated CRISPR delivery...
_Smith J et al. — Nature (2026)_
PMID: 12345678
pubmed.ncbi.nlm.nih.gov/12345678
*2.* AAV-based CRISPR therapeutics: advances and challenges
_Chen L et al. — Cell (2026)_
PMID: 12345679
pubmed.ncbi.nlm.nih.gov/12345679
```
### 5. Advanced searches
Support these query patterns:
- `"CRISPR"[Title] AND "delivery"[Title]` — title-specific
- `"2026"[Date - Publication]` — date filter
- `"Nature"[Journal]` — journal filter
- `review[Publication Type]` — type filter
### 6. Follow-up suggestions
After showing results, suggest:
- "Want me to summarize any of these papers?"
- "Should I search with different keywords?"
- "Want me to find related papers to any of these?"
---
name: pubmed-search-7ca93281c99d
description: Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. Triggers on "pubmed", "papers", "literature", "publications", "research on", "studies about".
---
# PubMed Search
Search NCBI PubMed for scientific literature using BioPython's Entrez module.
## When to Use
- User asks to find papers on a topic
- User wants recent publications in a field
- User asks for references or citations
- User wants to know the state of research on a topic
## How to Execute
### 1. Set up Entrez
```python
from Bio import Entrez
Entrez.email = "bioclaw@example.com"
```
### 2. Search PubMed
```python
# Search
handle = Entrez.esearch(db="pubmed", term="CRISPR delivery methods", retmax=20, sort="date")
record = Entrez.read(handle)
handle.close()
id_list = record["IdList"]
print(f"Found {record['Count']} results, showing top {len(id_list)}")
```
### 3. Fetch article details
```python
# Fetch details
handle = Entrez.efetch(db="pubmed", id=id_list, rettype="xml")
records = Entrez.read(handle)
handle.close()
for article in records['PubmedArticle']:
medline = article['MedlineCitation']
pmid = str(medline['PMID'])
title = medline['Article']['ArticleTitle']
# Get authors
authors = medline['Article'].get('AuthorList', [])
first_author = f"{authors[0].get('LastName', '')} {authors[0].get('Initials', '')}" if authors else "Unknown"
# Get journal and year
journal = medline['Article']['Journal']['Title']
pub_date = medline['Article']['Journal']['JournalIssue'].get('PubDate', {})
year = pub_date.get('Year', 'N/A')
# Get abstract
abstract_parts = medline['Article'].get('Abstract', {}).get('AbstractText', [])
abstract = ' '.join(str(a) for a in abstract_parts)[:300]
print(f"PMID: {pmid}")
print(f"Title: {title}")
print(f"Authors: {first_author} et al.")
print(f"Journal: {journal} ({year})")
print(f"Abstract: {abstract}...")
print(f"Link: https://pubmed.ncbi.nlm.nih.gov/{pmid}/")
print()
```
### 4. Output format for WhatsApp
```
*PubMed Search: "CRISPR delivery methods"*
_Found 1,234 results. Top 5:_
*1.* Lipid nanoparticle-mediated CRISPR delivery...
_Smith J et al. — Nature (2026)_
PMID: 12345678
pubmed.ncbi.nlm.nih.gov/12345678
*2.* AAV-based CRISPR therapeutics: advances and challenges
_Chen L et al. — Cell (2026)_
PMID: 12345679
pubmed.ncbi.nlm.nih.gov/12345679
```
### 5. Advanced searches
Support these query patterns:
- `"CRISPR"[Title] AND "delivery"[Title]` — title-specific
- `"2026"[Date - Publication]` — date filter
- `"Nature"[Journal]` — journal filter
- `review[Publication Type]` — type filter
### 6. Follow-up suggestions
After showing results, suggest:
- "Want me to summarize any of these papers?"
- "Should I search with different keywords?"
- "Want me to find related papers to any of these?"
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.