Measurement Error Analysis
SkillDev toolsAnalyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Measurement Error Analysis skill
What this skill tells your AI
The instructions your AI receives, as published by internscience/scp in skills/measurement-error-analysis/SKILL.md and read by ahel’s review.
Usage
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
import numpy as np
class AnalysisClient:
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
try:
self.transport = streamablehttp_client(url=self.server_url, headers={"SCP-HUB-API-KEY": self.api_key})
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
return True
except:
return False
async def disconnect(self):
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
return json.loads(result.content[0].text)
return str(result)
except:
return {"error": "parse error"}
## Initialize and use
client = AnalysisClient("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", "<your-api-key>")
await client.connect()
# Analyze measurement errors
measurements = [10.2, 10.5, 10.1, 10.4, 10.3]
mean = np.mean(measurements)
std_dev = np.std(measurements, ddof=1)
std_error = std_dev / np.sqrt(len(measurements))
print(f"Mean: {mean:.2f}")
print(f"Standard deviation: {std_dev:.3f}")
print(f"Standard error: {std_error:.3f}")
print(f"Result: {mean:.2f} ± {std_error:.3f}")
await client.disconnect()
Use Cases
- Experimental physics, quality control, calibration, uncertainty quantification
Signals
- GitHub stars
- 167
- Forks
- 9
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
measurement-error-analysis- Source
- github.com/internscience/scp