experimental-data-processing

Experimental Data Processing - Process experimental data: absolute error, mean square, max value, scientific notation formatting. Use this skill for experimental physics tasks involving calculate absolute error calculate mean square calculate max value format scientific notation convert to scientific notation. Combines 5 tools from 1 SCP server(s).

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Repository: InternScience/DrClaw
Letzte Quellaktivität: 13. März 2026 um 15:08
Erkannte Sprache von SKILL.md: Englisch
Sterne: 170
Forks: 9

Installationsoptionen

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npx skills add https://github.com/InternScience/DrClaw --skill experimental-data-processing

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SKILL.md

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name	experimental_data_processing
description	Experimental Data Processing - Process experimental data: absolute error, mean square, max value, scientific notation formatting. Use this skill for experimental physics tasks involving calculate absolute error calculate mean square calculate max value format scientific notation convert to scientific notation. Combines 5 tools from 1 SCP server(s).
i18n	{"zh":{"description":"实验数据处理：绝对误差、均方、。"}}

Experimental Data Processing

Discipline: Experimental Physics | Tools Used: 5 | Servers: 1

Description

Process experimental data: absolute error, mean square, max value, scientific notation formatting.

Tools Used

calculate_absolute_error from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
calculate_mean_square from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
calculate_max_value from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
format_scientific_notation from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
from (streamable-http) -

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-26": "https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis" } 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-26"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", "streamable-http") # Execute workflow steps # Step 1: Calculate absolute errors result_1 = await sessions["server-26"].call_tool("calculate_absolute_error", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Compute mean square result_2 = await sessions["server-26"].call_tool("calculate_mean_square", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Find maximum result_3 = await sessions["server-26"].call_tool("calculate_max_value", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Format in scientific notation result_4 = await sessions["server-26"].call_tool("format_scientific_notation", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Step 5: Summarize results result_5 = await sessions["server-26"].call_tool("convert_to_scientific_notation", 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())

experimental-data-processing

Installationsoptionen

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Experimental Data Processing

Description

Tools Used

Experimental Data Processing

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example

experimental-data-processing

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Mehr aus diesem Repository

Experimental Data Processing

Description

Tools Used

Mehr aus diesem Repository

Experimental Data Processing

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example