drugsda-esmfold

Use ESMFold model to predict 3D structure of the input protein sequence.

Best use case

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

Use ESMFold model to predict 3D structure of the input protein sequence.

Teams using drugsda-esmfold 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/drugsda-esmfold/SKILL.md --create-dirs "https://raw.githubusercontent.com/SpectrAI-Initiative/InnoClaw/main/.claude/skills/drugsda-esmfold/SKILL.md"

Manual Installation

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

How drugsda-esmfold Compares

Feature / Agentdrugsda-esmfoldStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Use ESMFold model to predict 3D structure of the input protein sequence.

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

# Protein Structure Prediction

## Usage

### 1. MCP Server Definition

```python
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class DrugSDAClient:    
    def __init__(self, server_url: str):
        self.server_url = server_url
        self.session = None
        
    async def connect(self):
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
            )
            self._stack = AsyncExitStack()
            await self._stack.__aenter__()
            self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
            
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self._stack.enter_async_context(self.session_ctx)

            await self.session.initialize()
            session_id = self.get_session_id()
            
            print(f"✓ connect success")
            return True
            
        except Exception as e:
            print(f"✗ connect failure: {e}")
            import traceback
            traceback.print_exc()
            return False
    
    async def disconnect(self):
        """Disconnect from server"""
        try:
            if hasattr(self, '_stack'):
                await self._stack.aclose()
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")
    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}
```

### 2. ESMFold

The description of tool *pred_protein_structure_esmfold*.

```tex
Use the ESMFold model for protein 3D structure prediction.
Args:
    sequence (str): Protein sequence
Return:
    status: success/error
    msg: message
    pdb_path (str): The predicted pdb file path
```

How to use tool *pred_protein_structure_esmfold* :

```python
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return

response = await client.session.call_tool(
    "pred_protein_structure_esmfold",
    arguments={
        "sequence": sequence
    }
)
result = client.parse_result(response)
pred_protein_structure = result["pdb_path"]

await client.disconnect() 
```

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