Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
BY Runchuan-BUUPDATED Oct 30, 2026
INTENDED FOR✳Claude Code>_Codex◈OpenClaw↗CursorInstall in a listed agent.
instructions.md
---
name: query-geo-63acde4ab3a2
description: Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
---
# NCBI GEO Database Query
Query Gene Expression Omnibus for public expression datasets.
## When to Use
- User wants to find RNA-seq or microarray datasets
- User asks about gene expression studies for a disease/tissue
- User provides a GEO accession (GSE/GDS) to look up
- User wants to download expression data
## How to Execute
```python
from Bio import Entrez
import json
Entrez.email = "bioclaw@example.com"
# 1. Search GEO datasets
def search_geo(query, max_results=10, db="gds"):
handle = Entrez.esearch(db=db, term=query, retmax=max_results, sort="relevance")
record = Entrez.read(handle)
handle.close()
return record
# 2. Get dataset summaries
def geo_summary(id_list, db="gds"):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.esummary(db=db, id=ids, retmode="json")
result = json.loads(handle.read())
handle.close()
return result
# 3. Search for Series (GSE)
def search_gse(keyword, organism="Homo sapiens", max_results=10):
query = f'"{keyword}" AND "{organism}"[Organism] AND gse[ETYP]'
return search_geo(query, max_results)
# Example: Find breast cancer RNA-seq datasets
search = search_gse("breast cancer RNA-seq", max_results=5)
print(f"Found {search['Count']} datasets")
if search['IdList']:
summaries = geo_summary(search['IdList'])
for uid in search['IdList']:
info = summaries['result'].get(str(uid), {})
title = info.get('title', 'N/A')
gse = info.get('accession', 'N/A')
gpl = info.get('gpl', 'N/A')
n_samples = info.get('n_samples', 'N/A')
summary = info.get('summary', 'N/A')[:200]
print(f"\n{gse}: {title}")
print(f" Platform: {gpl}, Samples: {n_samples}")
print(f" Summary: {summary}...")
print(f" URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={gse}")
```
## Search Syntax
- By keyword: `"CRISPR" AND gse[ETYP]`
- By organism: `"Homo sapiens"[Organism]`
- By platform: `"Illumina"[Platform]`
- By date: `"2024/01:2026/12"[PDAT]`
- Combine: `"breast cancer" AND "RNA-seq" AND "Homo sapiens"[Organism] AND gse[ETYP]`
## Follow-up Suggestions
- "Want me to download the expression matrix for this dataset?"
- "Should I do differential expression analysis?"
- "Want me to check what genes are differentially expressed?"
---
name: query-geo-63acde4ab3a2
description: Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
---
# NCBI GEO Database Query
Query Gene Expression Omnibus for public expression datasets.
## When to Use
- User wants to find RNA-seq or microarray datasets
- User asks about gene expression studies for a disease/tissue
- User provides a GEO accession (GSE/GDS) to look up
- User wants to download expression data
## How to Execute
```python
from Bio import Entrez
import json
Entrez.email = "bioclaw@example.com"
# 1. Search GEO datasets
def search_geo(query, max_results=10, db="gds"):
handle = Entrez.esearch(db=db, term=query, retmax=max_results, sort="relevance")
record = Entrez.read(handle)
handle.close()
return record
# 2. Get dataset summaries
def geo_summary(id_list, db="gds"):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.esummary(db=db, id=ids, retmode="json")
result = json.loads(handle.read())
handle.close()
return result
# 3. Search for Series (GSE)
def search_gse(keyword, organism="Homo sapiens", max_results=10):
query = f'"{keyword}" AND "{organism}"[Organism] AND gse[ETYP]'
return search_geo(query, max_results)
# Example: Find breast cancer RNA-seq datasets
search = search_gse("breast cancer RNA-seq", max_results=5)
print(f"Found {search['Count']} datasets")
if search['IdList']:
summaries = geo_summary(search['IdList'])
for uid in search['IdList']:
info = summaries['result'].get(str(uid), {})
title = info.get('title', 'N/A')
gse = info.get('accession', 'N/A')
gpl = info.get('gpl', 'N/A')
n_samples = info.get('n_samples', 'N/A')
summary = info.get('summary', 'N/A')[:200]
print(f"\n{gse}: {title}")
print(f" Platform: {gpl}, Samples: {n_samples}")
print(f" Summary: {summary}...")
print(f" URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={gse}")
```
## Search Syntax
- By keyword: `"CRISPR" AND gse[ETYP]`
- By organism: `"Homo sapiens"[Organism]`
- By platform: `"Illumina"[Platform]`
- By date: `"2024/01:2026/12"[PDAT]`
- Combine: `"breast cancer" AND "RNA-seq" AND "Homo sapiens"[Organism] AND gse[ETYP]`
## Follow-up Suggestions
- "Want me to download the expression matrix for this dataset?"
- "Should I do differential expression analysis?"
- "Want me to check what genes are differentially expressed?"
COMPATIBILITY & REFERENCES
Platform guidance
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.
---name: liquid-biopsy-analytics-agent description: AI-powered comprehensive liquid biopsy analysis integrating ctDNA, CTCs, exosomes, and cfRNA for cancer detection, monitoring, and treatment guidance. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:
Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies ...
Generate reproducible bioinformatics/data-science Methods + run documentation from workflow run artifacts (Nextflow/Snakemake/CWL), including a top-of-doc workflow summary, exact executed commands, tool versions, parameters, QC, and outputs.