rare_disease_genetics
Rare Disease Genetic Analysis - Analyze rare disease genetics: Monarch phenotype-disease mapping, ClinVar variants, NCBI gene data, and OpenTargets. Use this skill for rare disease genetics tasks involving get HPO ID by phenotype get joint associated diseases by HPO ID list clinvar search get associated targets by disease efoId. Combines 4 tools from 3 SCP server(s).
Best use case
rare_disease_genetics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Rare Disease Genetic Analysis - Analyze rare disease genetics: Monarch phenotype-disease mapping, ClinVar variants, NCBI gene data, and OpenTargets. Use this skill for rare disease genetics tasks involving get HPO ID by phenotype get joint associated diseases by HPO ID list clinvar search get associated targets by disease efoId. Combines 4 tools from 3 SCP server(s).
Teams using rare_disease_genetics 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/rare_disease_genetics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rare_disease_genetics Compares
| Feature / Agent | rare_disease_genetics | 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?
Rare Disease Genetic Analysis - Analyze rare disease genetics: Monarch phenotype-disease mapping, ClinVar variants, NCBI gene data, and OpenTargets. Use this skill for rare disease genetics tasks involving get HPO ID by phenotype get joint associated diseases by HPO ID list clinvar search get associated targets by disease efoId. Combines 4 tools from 3 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
# Rare Disease Genetic Analysis
**Discipline**: Rare Disease Genetics | **Tools Used**: 4 | **Servers**: 3
## Description
Analyze rare disease genetics: Monarch phenotype-disease mapping, ClinVar variants, NCBI gene data, and OpenTargets.
## Tools Used
- **`get_HPO_ID_by_phenotype`** from `monarch-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch`
- **`get_joint_associated_diseases_by_HPO_ID_list`** from `monarch-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch`
- **`clinvar_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search`
- **`get_associated_targets_by_disease_efoId`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets`
## Workflow
1. Get HPO ID for phenotype
2. Find associated diseases
3. Search ClinVar for pathogenic variants
4. Get OpenTargets target associations
## Test Case
### Input
```json
{
"phenotype": "seizures",
"hpo_ids": [
"HP:0001250"
]
}
```
### Expected Steps
1. Get HPO ID for phenotype
2. Find associated diseases
3. Search ClinVar for pathogenic variants
4. Get OpenTargets target associations
## Usage Example
> **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` 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 contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"monarch-server": "https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch",
"search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search",
"opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
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():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["monarch-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch", stack)
sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack)
sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack)
# Execute workflow steps
# Step 1: Get HPO ID for phenotype
result_1 = await sessions["monarch-server"].call_tool("get_HPO_ID_by_phenotype", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Find associated diseases
result_2 = await sessions["monarch-server"].call_tool("get_joint_associated_diseases_by_HPO_ID_list", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Search ClinVar for pathogenic variants
result_3 = await sessions["search-server"].call_tool("clinvar_search", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get OpenTargets target associations
result_4 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", 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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