antibody_drug_development
Antibody Drug Development - Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. Use this skill for biologics tasks involving get uniprotkb entry by accession get biotherapeutic by name ComputeProtPara ComputeHydrophilicity. Combines 4 tools from 3 SCP server(s).
Best use case
antibody_drug_development is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Antibody Drug Development - Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. Use this skill for biologics tasks involving get uniprotkb entry by accession get biotherapeutic by name ComputeProtPara ComputeHydrophilicity. Combines 4 tools from 3 SCP server(s).
Teams using antibody_drug_development 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/antibody_drug_development/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How antibody_drug_development Compares
| Feature / Agent | antibody_drug_development | 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?
Antibody Drug Development - Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. Use this skill for biologics tasks involving get uniprotkb entry by accession get biotherapeutic by name ComputeProtPara ComputeHydrophilicity. 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.
Related Guides
SKILL.md Source
# Antibody Drug Development
**Discipline**: Biologics | **Tools Used**: 4 | **Servers**: 3
## Description
Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction.
## Tools Used
- **`get_uniprotkb_entry_by_accession`** from `uniprot-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt`
- **`get_biotherapeutic_by_name`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL`
- **`ComputeProtPara`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio`
- **`ComputeHydrophilicity`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio`
## Workflow
1. Get target protein info
2. Look up biotherapeutic in ChEMBL
3. Compute protein parameters
4. Analyze hydrophilicity
## Test Case
### Input
```json
{
"target_accession": "P04637",
"biotherapeutic": "trastuzumab",
"sequence": "MKTIIALSYIFCLVFA"
}
```
### Expected Steps
1. Get target protein info
2. Look up biotherapeutic in ChEMBL
3. Compute protein parameters
4. Analyze hydrophilicity
## 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 = {
"uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
"server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio"
}
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["uniprot-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "streamable-http")
sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
sessions["server-29"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", "sse")
# Execute workflow steps
# Step 1: Get target protein info
result_1 = await sessions["uniprot-server"].call_tool("get_uniprotkb_entry_by_accession", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Look up biotherapeutic in ChEMBL
result_2 = await sessions["chembl-server"].call_tool("get_biotherapeutic_by_name", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Compute protein parameters
result_3 = await sessions["server-29"].call_tool("ComputeProtPara", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Analyze hydrophilicity
result_4 = await sessions["server-29"].call_tool("ComputeHydrophilicity", 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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