pubchem_deep_dive

PubChem Deep Dive Analysis - Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description. Use this skill for chemical databases tasks involving get pubchem compound by cid get assay summary by cid get conformers by cid get compound synonyms by name get general info by compound name. Combines 5 tools from 1 SCP server(s).

Best use case

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

PubChem Deep Dive Analysis - Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description. Use this skill for chemical databases tasks involving get pubchem compound by cid get assay summary by cid get conformers by cid get compound synonyms by name get general info by compound name. Combines 5 tools from 1 SCP server(s).

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

Manual Installation

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

How pubchem_deep_dive Compares

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

Frequently Asked Questions

What does this skill do?

PubChem Deep Dive Analysis - Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description. Use this skill for chemical databases tasks involving get pubchem compound by cid get assay summary by cid get conformers by cid get compound synonyms by name get general info by compound name. Combines 5 tools from 1 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

# PubChem Deep Dive Analysis

**Discipline**: Chemical Databases | **Tools Used**: 5 | **Servers**: 1

## Description

Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description.

## Tools Used

- **`get_pubchem_compound_by_cid`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_assay_summary_by_cid`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_conformers_by_cid`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_compound_synonyms_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_general_info_by_compound_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`

## Workflow

1. Get full compound info
2. Get bioassay summary
3. Get 3D conformers
4. Get all synonyms
5. Get general description

## Test Case

### Input
```json
{
    "compound_name": "aspirin",
    "cid": 2244
}
```

### Expected Steps
1. Get full compound info
2. Get bioassay summary
3. Get 3D conformers
4. Get all synonyms
5. Get general description

## 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 = {
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem"
}

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["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack)

        # Execute workflow steps
        # Step 1: Get full compound info
        result_1 = await sessions["pubchem-server"].call_tool("get_pubchem_compound_by_cid", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get bioassay summary
        result_2 = await sessions["pubchem-server"].call_tool("get_assay_summary_by_cid", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Get 3D conformers
        result_3 = await sessions["pubchem-server"].call_tool("get_conformers_by_cid", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Get all synonyms
        result_4 = await sessions["pubchem-server"].call_tool("get_compound_synonyms_by_name", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Step 5: Get general description
        result_5 = await sessions["pubchem-server"].call_tool("get_general_info_by_compound_name", arguments={})
        data_5 = parse(result_5)
        print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())
```

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