菜单
Genome Annotation Pipeline - Annotate a genome: NCBI annotation report, Ensembl gene lookup, UCSC tracks, and KEGG pathway links. Use this skill for genomics tasks involving get genome annotation report get lookup symbol list tracks kegg link. Combines 4 tools from 4 SCP server(s).
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/InternScience/DrClaw --skill genome-annotation命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | genome_annotation |
| description | Genome Annotation Pipeline - Annotate a genome: NCBI annotation report, Ensembl gene lookup, UCSC tracks, and KEGG pathway links. Use this skill for genomics tasks involving get genome annotation report get lookup symbol list tracks kegg link. Combines 4 tools from 4 SCP server(s). |
| i18n | {"zh":{"description":"基因组注释：NCBI、Ense。"}} |
Discipline: Genomics | Tools Used: 4 | Servers: 4
Annotate a genome: NCBI annotation report, Ensembl gene lookup, UCSC tracks, and KEGG pathway links.
get_genome_annotation_report from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIget_lookup_symbol from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembllist_tracks from ucsc-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSCkegg_link from kegg-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG{
"accession": "GCF_000001405.40",
"gene_symbol": "BRCA1",
"genome": "hg38"
}
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 = {
"ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
"ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
"ucsc-server": "https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC",
"kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG"
}
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["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http")
sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
sessions["ucsc-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC", "streamable-http")
sessions["kegg-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", "streamable-http")
# Execute workflow steps
# Step 1: Get NCBI genome annotation
result_1 = await sessions["ncbi-server"].call_tool("get_genome_annotation_report", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Look up gene in Ensembl
result_2 = await sessions["ensembl-server"].call_tool("get_lookup_symbol", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: List UCSC tracks
result_3 = await sessions["ucsc-server"].call_tool("list_tracks", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Link to KEGG pathways
result_4 = await sessions["kegg-server"].call_tool("kegg_link", 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())