skills/cross_species_genomics/SKILL.md
Cross-Species Comparative Genomics - Compare genomes across species: Ensembl compara, alignment, gene trees, and NCBI taxonomy. Use this skill for comparative genomics tasks involving get info compara species sets get alignment region get genetree member symbol get taxonomy. Combines 4 tools from 2 SCP server(s).
npx skillsauth add InternScience/scp cross_species_genomicsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Discipline: Comparative Genomics | Tools Used: 4 | Servers: 2
Compare genomes across species: Ensembl compara, alignment, gene trees, and NCBI taxonomy.
get_info_compara_species_sets from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensemblget_alignment_region from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensemblget_genetree_member_symbol from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensemblget_taxonomy from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI{
"gene": "BRCA1",
"species": "homo_sapiens",
"region": "17:43044295-43125370"
}
Note: Replace
<YOUR_SCP_HUB_API_KEY>with your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
"ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI"
}
async def connect(url, transport_type):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
read, write, _ = await transport.__aenter__()
ctx = ClientSession(read, write)
session = await ctx.__aenter__()
await session.initialize()
return session, ctx, transport
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
# Connect to required servers
sessions = {}
sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
sessions["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http")
# Execute workflow steps
# Step 1: Get compara species sets
result_1 = await sessions["ensembl-server"].call_tool("get_info_compara_species_sets", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get genomic alignment
result_2 = await sessions["ensembl-server"].call_tool("get_alignment_region", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get gene tree
result_3 = await sessions["ensembl-server"].call_tool("get_genetree_member_symbol", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get taxonomy info
result_4 = await sessions["ncbi-server"].call_tool("get_taxonomy", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
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