sciagent-skill-creator
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Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or
$ npx -y skills add jaechang-hits/SciAgent-Skills --skill cosmic-database --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cosmic-databaseContext preview
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Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or
name: "cosmic-database" description: "Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity." license: "CC-BY-NC-SA-4.0"
COSMIC (Catalogue Of Somatic Mutations In Cancer) is the world's largest expert-curated database of somatic mutations in cancer, covering 6.7M+ coding mutations, 40,000+ cancer samples, 19,000+ genes across all cancer types. It includes the Cancer Gene Census (critical cancer genes), mutational signatures (SBS, DBS, ID), drug resistance variants, copy number data, gene expression, and methylation. The REST API v3.1 enables programmatic queries; most features are freely accessible after registration.
pip install requests pandas # Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials
import requests
import base64
# COSMIC API requires base64-encoded email:password authentication
EMAIL = "your_registered@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()
BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}
# Get mutations for KRAS gene
r = requests.get(f"{BASE}/mutations",
headers=HEADERS,
params={"gene_name": "KRAS", "limit": 5})
r.raise_for_status()
data = r.json()
print(f"Total KRAS mutations: {data['meta']['total']}")
for m in data["data"][:3]:
print(f" {m['mutation_id']:15s} AA: {m.get('mutation_aa')} | Cancer: {m.get('primary_site')}")Retrieve all COSMIC somatic mutations for a gene, with cancer type and amino acid change.
import requests, base64, pandas as pd
EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()
BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}
def get_gene_mutations(gene, limit=100, cancer_site=None):
params = {"gene_name": gene, "limit": limit}
if cancer_site:
params["primary_site"] = cancer_site
r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params)
r.raise_for_status()
return r.json()
data = get_gene_mutations("TP53", limit=20)
print(f"Total TP53 mutations in COSMIC: {data['meta']['total']}")
rows = []
for m in data["data"][:10]:
rows.append({
"mutation_id": m.get("mutation_id"),
"mutation_aa": m.get("mutation_aa"),
"mutation_cds": m.get("mutation_cds"),
"primary_site": m.get("primary_site"),
"histology": m.get("primary_histology"),
"count": m.get("count"),
})
df = pd.DataFrame(rows)
print(df.head())# Filter by cancer site
data_lung = get_gene_mutations("TP53", cancer_site="lung", limit=20)
print(f"\nTP53 mutations in lung cancer: {data_lung['meta']['total']}")Retrieve the COSMIC Cancer Gene Census — classified cancer driver genes.
import requests, base64, pandas as pd
EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()
BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}
r = requests.get(f"{BASE}/genes", headers=HEADERS, params={"limit": 100})
r.raise_for_status()
data = r.json()
print(f"Total genes in COSMIC: {data['meta']['total']}")
# Get Cancer Gene Census genes
r_cgc = requests.get(f"{BASE}/genes",
headers=HEADERS,
params={"cgc_tier": "1", "limit": 50})
cgc_data = r_cgc.json()
print(f"\nCGC Tier 1 genes: {cgc_data['meta']['total']}")
rows = []
for g in cgc_data["data"][:15]:
rows.append({
"gene": g.get("gene_name"),
"tier": g.get("cgc_tier"),
"role": g.get("role_in_cancer"),
"mutation_types": g.get("mutation_types"),
"tumour_types": str(g.get("tumour_types_somatic", []))[:80],
})
df = pd.DataFrame(rows)
print(df.to_string(index=False))Retrieve details for a known COSMIC mutation ID (COSM…).
import requests, base64
EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()
BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}
# KRAS G12D mutation
mutation_id = "COSM521"
r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS)
r.raise_for_status()
m = r.json()
print(f"Mutation ID : {m.get('mutation_id')}")
print(f"Gene : {m.get('gene_name')}")
print(f"AA change : {m.get('mutation_aa')}")
print(f"CDS change :Turn your AI coding agent into a life sciences expert — 199 bioinformatics skills for Claude Code covering RNA-seq, single-cell analysis, genomics, proteomics, drug discovery, and more. Boosted BixBench from 65% to 92%. Open source.
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