functional_group_profiling

Functional Group Profiling - Profile functional groups: radical assignment, H-bond analysis, aromaticity, and abbreviation condensation. Use this skill for organic chemistry tasks involving AssignRadicals GetHBANum AromaticityAnalyzer CondenseAbbreviationSubstanceGroups. Combines 4 tools from 2 SCP server(s).

157 stars

Best use case

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

Functional Group Profiling - Profile functional groups: radical assignment, H-bond analysis, aromaticity, and abbreviation condensation. Use this skill for organic chemistry tasks involving AssignRadicals GetHBANum AromaticityAnalyzer CondenseAbbreviationSubstanceGroups. Combines 4 tools from 2 SCP server(s).

Teams using functional_group_profiling 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/functional_group_profiling/SKILL.md --create-dirs "https://raw.githubusercontent.com/InternScience/DrClaw/main/drclaw/local_skill_hub/science/chemistry/functional_group_profiling/SKILL.md"

Manual Installation

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

How functional_group_profiling Compares

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

Frequently Asked Questions

What does this skill do?

Functional Group Profiling - Profile functional groups: radical assignment, H-bond analysis, aromaticity, and abbreviation condensation. Use this skill for organic chemistry tasks involving AssignRadicals GetHBANum AromaticityAnalyzer CondenseAbbreviationSubstanceGroups. Combines 4 tools from 2 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

# Functional Group Profiling

**Discipline**: Organic Chemistry | **Tools Used**: 4 | **Servers**: 2

## Description

Profile functional groups: radical assignment, H-bond analysis, aromaticity, and abbreviation condensation.

## Tools Used

- **`AssignRadicals`** from `server-31` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem`
- **`GetHBANum`** from `server-31` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem`
- **`AromaticityAnalyzer`** from `server-28` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent`
- **`CondenseAbbreviationSubstanceGroups`** from `server-31` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem`

## Workflow

1. Assign radical sites
2. Count H-bond acceptors
3. Analyze aromaticity
4. Condense substance groups

## Test Case

### Input
```json
{
    "smiles": "CC(=O)Oc1ccccc1C(=O)O"
}
```

### Expected Steps
1. Assign radical sites
2. Count H-bond acceptors
3. Analyze aromaticity
4. Condense substance groups

## 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 = {
    "server-31": "https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem",
    "server-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent"
}

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["server-31"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem", "sse")
    sessions["server-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse")

    # Execute workflow steps
    # Step 1: Assign radical sites
    result_1 = await sessions["server-31"].call_tool("AssignRadicals", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Count H-bond acceptors
    result_2 = await sessions["server-31"].call_tool("GetHBANum", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Analyze aromaticity
    result_3 = await sessions["server-28"].call_tool("AromaticityAnalyzer", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Condense substance groups
    result_4 = await sessions["server-31"].call_tool("CondenseAbbreviationSubstanceGroups", 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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