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population_genetics: Скилл анализа популяционной генетики
SKILL.md
readonly
--- lines
---
name: population_genetics
description: "Population Genetics Analysis - Analyze population genetics: Ensembl variation populations, linkage disequilibrium, and variant frequency data. Use this skill for population genetics tasks involving get info variation populations get ld get variation get variant recoder. Combines 4 tools from 1 SCP server(s)."
---
# Population Genetics Analysis
**Discipline**: Population Genetics | **Tools Used**: 4 | **Servers**: 1
## Description
Analyze population genetics: Ensembl variation populations, linkage disequilibrium, and variant frequency data.
## Tools Used
- **`get_info_variation_populations`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
- **`get_ld`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
- **`get_variation`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
- **`get_variant_recoder`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
## Workflow
1. Get variation populations
2. Calculate LD for variant
3. Get variant details
4. Recode variant identifiers
## Test Case
### Input
```json
{
"variant_id": "rs699",
"species": "homo_sapiens",
"population": "1000GENOMES:phase_3:CEU"
}
```
### Expected Steps
1. Get variation populations
2. Calculate LD for variant
3. Get variant details
4. Recode variant identifiers
## 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 = {
"ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl"
}
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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack)
# Execute workflow steps
# Step 1: Get variation populations
result_1 = await sessions["ensembl-server"].call_tool("get_info_variation_populations", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Calculate LD for variant
result_2 = await sessions["ensembl-server"].call_tool("get_ld", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get variant details
result_3 = await sessions["ensembl-server"].call_tool("get_variation", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Recode variant identifiers
result_4 = await sessions["ensembl-server"].call_tool("get_variant_recoder", 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())
```
Инициализация мануала...
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package.json
$ install --global
skills.sh
npx skills add https://github.com/SpectrAI-Initiative/InnoClaw/tree/main/.claude/skills/population_genetics
$ download --local
man
[HINT] Скачивает всю директорию скилла с GitHub: SKILL.md и все связанные файлы