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Agent skill

Measurement Error Analysis

by InternScience in InternScience/scp

Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.

MITAuto-check passed

Install Measurement Error Analysis

skills CLI
$ npx skills add InternScience/scp --skill measurement-error-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install InternScience/scp measurement-error-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/measurement-error-analysis .claude/skills/measurement-error-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
measurement-error-analysis
GitHub stars
169
Used in
1 other repo
Token cost
~553 tokens
SKILL.md length
13 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.

  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Measurement Error Analysis is an agent skill from InternScience/scp. Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.

Its SKILL.md is about 550 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is MIT.

Example prompts

  • “/measurement-error-analysis”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Measurement Error Analysis loads about 553 tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 13 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~553

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 13 words, ~553 tokens.

Download SKILL.mdSave it as .claude/skills/measurement-error-analysis/SKILL.md (or your agent's skills folder).
name
measurement-error-analysis
description
Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
license
MIT license
metadata.skill-author
PJLab

Measurement Error Analysis

Usage

python
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

© InternScience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/measurement-error-analysis of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Measurement Error Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Measurement Error Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Measurement Error Analysis this skillInternScience/scp1691 repos~553Automated safety check: PassMIT
Statistical Experimental Evaluationaiming-lab/AutoResearchClaw15k—~553Automated safety check: PassMIT
Manuscript Statistics AuditYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0
Statistical PowerK-Dense-AI/scientific-agent-skills48k1 repos~4.4kAutomated safety check: NotesMIT
Statistical Analystalirezarezvani/claude-skills28k1 repos~2.5kAutomated safety check: PassMIT
Uncertainty And UnitsK-Dense-AI/scientific-agent-skills48k2 repos~5.4kAutomated safety check: NotesMIT

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Questions about Measurement Error Analysis

What does Measurement Error Analysis do?

Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control. Measurement Error Analysis is an agent skill from InternScience/scp. Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.

How do I install Measurement Error Analysis in Claude Code?

Run `npx skills add InternScience/scp --skill measurement-error-analysis -a claude-code`. Or copy the skill folder (skills/measurement-error-analysis in InternScience/scp) into .claude/skills/measurement-error-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Measurement Error Analysis in Codex?

Run `npx skills add InternScience/scp --skill measurement-error-analysis -a codex`. Or copy the skill folder (skills/measurement-error-analysis in InternScience/scp) into .agents/skills/measurement-error-analysis in your project. Codex loads it when a task matches its description.

Can I use Measurement Error Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add InternScience/scp --skill measurement-error-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/measurement-error-analysis, .gemini/skills/measurement-error-analysis, .github/skills/measurement-error-analysis and .opencode/skills/measurement-error-analysis in your project.

What does Measurement Error Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Measurement Error Analysis is instructions for the agent only. Our summary lists: Python 3.

Does Measurement Error Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Measurement Error Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Measurement Error Analysis use?

Measurement Error Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Measurement Error Analysis use?

About 553 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Measurement Error Analysis?

Skills that share tags, products or a category with Measurement Error Analysis: Statistical Experimental Evaluation (aiming-lab/AutoResearchClaw, 15k stars), Manuscript Statistics Audit (Yuan1z0825/nature-skills, 46k stars), Statistical Power (K-Dense-AI/scientific-agent-skills, 48k stars) and Statistical Analyst (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Measurement Error Analysis?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 169 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.