gene_variant_drug_nexus

Gene-Variant-Drug Nexus - Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence. Use this skill for translational genomics tasks involving get vep hgvs get associated targets by disease efoId get associated drugs by target name clinvar search. Combines 4 tools from 3 SCP server(s).

157 stars

Best use case

gene_variant_drug_nexus is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Gene-Variant-Drug Nexus - Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence. Use this skill for translational genomics tasks involving get vep hgvs get associated targets by disease efoId get associated drugs by target name clinvar search. Combines 4 tools from 3 SCP server(s).

Teams using gene_variant_drug_nexus 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

$curl -o ~/.claude/skills/gene_variant_drug_nexus/SKILL.md --create-dirs "https://raw.githubusercontent.com/InternScience/DrClaw/main/drclaw/local_skill_hub/science/drug/gene_variant_drug_nexus/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/gene_variant_drug_nexus/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How gene_variant_drug_nexus Compares

Feature / Agentgene_variant_drug_nexusStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Gene-Variant-Drug Nexus - Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence. Use this skill for translational genomics tasks involving get vep hgvs get associated targets by disease efoId get associated drugs by target name clinvar search. 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

# Gene-Variant-Drug Nexus

**Discipline**: Translational Genomics | **Tools Used**: 4 | **Servers**: 3

## Description

Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence.

## Tools Used

- **`get_vep_hgvs`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
- **`get_associated_targets_by_disease_efoId`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets`
- **`get_associated_drugs_by_target_name`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets`
- **`clinvar_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search`

## Workflow

1. Predict variant effect
2. Get disease-target associations
3. Find drugs for target
4. Check ClinVar clinical significance

## Test Case

### Input
```json
{
    "hgvs": "ENSP00000269305.4:p.Arg175His",
    "disease_efo": "EFO_0000311"
}
```

### Expected Steps
1. Predict variant effect
2. Get disease-target associations
3. Find drugs for target
4. Check ClinVar clinical significance

## 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 = {
    "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
    "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
    "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search"
}

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["opentargets-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "streamable-http")
    sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")

    # Execute workflow steps
    # Step 1: Predict variant effect
    result_1 = await sessions["ensembl-server"].call_tool("get_vep_hgvs", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Get disease-target associations
    result_2 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Find drugs for target
    result_3 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Check ClinVar clinical significance
    result_4 = await sessions["search-server"].call_tool("clinvar_search", 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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