drug_metabolism_study
Drug Metabolism Study - Study drug metabolism: FDA metabolism data, ChEMBL metabolism records, PubChem compound data, and clinical pharmacology. Use this skill for drug metabolism tasks involving get metabolism by id get pharmacokinetics by drug name get compound by name get clinical pharmacology by drug name. Combines 4 tools from 3 SCP server(s).
Best use case
drug_metabolism_study is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Drug Metabolism Study - Study drug metabolism: FDA metabolism data, ChEMBL metabolism records, PubChem compound data, and clinical pharmacology. Use this skill for drug metabolism tasks involving get metabolism by id get pharmacokinetics by drug name get compound by name get clinical pharmacology by drug name. Combines 4 tools from 3 SCP server(s).
Teams using drug_metabolism_study 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/drug_metabolism_study/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How drug_metabolism_study Compares
| Feature / Agent | drug_metabolism_study | 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?
Drug Metabolism Study - Study drug metabolism: FDA metabolism data, ChEMBL metabolism records, PubChem compound data, and clinical pharmacology. Use this skill for drug metabolism tasks involving get metabolism by id get pharmacokinetics by drug name get compound by name get clinical pharmacology by drug name. 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
# Drug Metabolism Study
**Discipline**: Drug Metabolism | **Tools Used**: 4 | **Servers**: 3
## Description
Study drug metabolism: FDA metabolism data, ChEMBL metabolism records, PubChem compound data, and clinical pharmacology.
## Tools Used
- **`get_metabolism_by_id`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL`
- **`get_pharmacokinetics_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug`
- **`get_compound_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_clinical_pharmacology_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug`
## Workflow
1. Get ChEMBL metabolism data
2. Get FDA pharmacokinetics
3. Get PubChem compound data
4. Get clinical pharmacology
## Test Case
### Input
```json
{
"drug_name": "warfarin",
"met_id": 1
}
```
### Expected Steps
1. Get ChEMBL metabolism data
2. Get FDA pharmacokinetics
3. Get PubChem compound data
4. Get clinical pharmacology
## 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 = {
"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
"fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug",
"pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem"
}
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["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
sessions["pubchem-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "streamable-http")
# Execute workflow steps
# Step 1: Get ChEMBL metabolism data
result_1 = await sessions["chembl-server"].call_tool("get_metabolism_by_id", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get FDA pharmacokinetics
result_2 = await sessions["fda-drug-server"].call_tool("get_pharmacokinetics_by_drug_name", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get PubChem compound data
result_3 = await sessions["pubchem-server"].call_tool("get_compound_by_name", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get clinical pharmacology
result_4 = await sessions["fda-drug-server"].call_tool("get_clinical_pharmacology_by_drug_name", 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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