ncbi_gene_deep_dive
NCBI Gene Deep Dive - Deep dive into NCBI gene: metadata, dataset report, product report, orthologs, and gene links. Use this skill for gene biology tasks involving get gene metadata by gene name get gene dataset report by id get gene product report by id get gene orthologs get gene links by id. Combines 5 tools from 1 SCP server(s).
Best use case
ncbi_gene_deep_dive is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
NCBI Gene Deep Dive - Deep dive into NCBI gene: metadata, dataset report, product report, orthologs, and gene links. Use this skill for gene biology tasks involving get gene metadata by gene name get gene dataset report by id get gene product report by id get gene orthologs get gene links by id. Combines 5 tools from 1 SCP server(s).
Teams using ncbi_gene_deep_dive should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ncbi_gene_deep_dive/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ncbi_gene_deep_dive Compares
| Feature / Agent | ncbi_gene_deep_dive | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
NCBI Gene Deep Dive - Deep dive into NCBI gene: metadata, dataset report, product report, orthologs, and gene links. Use this skill for gene biology tasks involving get gene metadata by gene name get gene dataset report by id get gene product report by id get gene orthologs get gene links by id. Combines 5 tools from 1 SCP server(s).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# NCBI Gene Deep Dive
**Discipline**: Gene Biology | **Tools Used**: 5 | **Servers**: 1
## Description
Deep dive into NCBI gene: metadata, dataset report, product report, orthologs, and gene links.
## Tools Used
- **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`get_gene_dataset_report_by_id`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`get_gene_product_report_by_id`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`get_gene_orthologs`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`get_gene_links_by_id`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
## Workflow
1. Get gene metadata
2. Get dataset report
3. Get product report
4. Get orthologs
5. Get gene links
## Test Case
### Input
```json
{
"gene_name": "TP53",
"gene_id": 7157
}
```
### Expected Steps
1. Get gene metadata
2. Get dataset report
3. Get product report
4. Get orthologs
5. Get gene links
## Usage Example
> **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).
```python
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"
}
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")
# Execute workflow steps
# Step 1: Get gene metadata
result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get dataset report
result_2 = await sessions["ncbi-server"].call_tool("get_gene_dataset_report_by_id", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get product report
result_3 = await sessions["ncbi-server"].call_tool("get_gene_product_report_by_id", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get orthologs
result_4 = await sessions["ncbi-server"].call_tool("get_gene_orthologs", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Step 5: Get gene links
result_5 = await sessions["ncbi-server"].call_tool("get_gene_links_by_id", arguments={})
data_5 = parse(result_5)
print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
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