datawhalechina

datawhalechina / agent-skills-with-anthropic

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本项目围绕吴恩达老师在DeepLearning.AI出品的agent-skills-with-anthropic系列课程，为学习者打造中文翻译与知识整理教程。项目提供课程内容翻译、知识点梳理和示例代码解读等内容，欢迎大家Star!

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Found Feb 07, 2026 at 29 stars 4x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Python scripts that analyze time series data from CSV files, providing diagnostic reports on stationarity, seasonality, trends, and forecastability, along with visualizations.

How It Works

1
📊 Gather your time data

You start with a simple spreadsheet file of dates and numbers, like daily sales over months.

2
🔍 Check your data's health

You run the easy checker tool on your file to scan for patterns and issues.

3
📋 Read the friendly report

You get a clear summary telling you if your data has trends, repeats over time, or needs tweaks.

4
🖼️ Make helpful pictures

You create charts showing your data's shape, ups and downs, and hidden rhythms.

5
💡 Spot the key insights

You discover if your numbers are steady, seasonal, or ready for future guesses.

🎉 Master your data

Now you understand your time patterns perfectly and can plan ahead confidently.

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AI-Generated Review

What is agent-skills-with-anthropic?

This GitHub repo translates and organizes Andrew Ng's DeepLearning.AI course on agent skills with Anthropic into Chinese, complete with knowledge summaries and runnable Python examples. It delivers a practical custom skill for time series analysis: feed it a CSV file via CLI, and it spits out JSON diagnostics, human-readable summaries, and plots covering stationarity tests, seasonality, trends, autocorrelation, and forecastability. Developers get a ready-to-use Anthropic agent skill for diagnosing time series data without building from scratch.

Why is it gaining traction?

In the agent skills Anthropic GitHub space, it stands out with concrete Python code that automates tedious time series checks—ADF/KPSS tests, STL decomposition, Ljung-Box for predictability—all via simple CLI flags like --output-dir or --seasonal-period. The bilingual tutorial hooks learners bridging English courses to hands-on skills, saving hours on stats boilerplate while integrating seamlessly with Anthropic agents.

Who should use this?

Data scientists prototyping Anthropic agents for sales forecasting or IoT monitoring, where quick time series diagnostics reveal if data is stationary or seasonal. Chinese-speaking ML engineers following Andrew Ng's course who want translated notes plus executable Python skills for custom agent tools.

Verdict

Grab it if you're in the niche—54 stars and 1.0% credibility reflect early-stage maturity with thin docs, but the CLI-driven diagnostics work reliably for prototyping. Solid starter for Anthropic agent skills, just add your own tests for production.

(178 words)

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