simonlin1212/global-stock-data
global-stock-data
美股港股全栈数据工具包 (AI Skill) — 7层架构 · 17端点 · 5数据源 · 零鉴权 | US & HK Stock Full-Stack Data Toolkit for AI Coding Assistants
Usage guide
global-stock-data is an open-source project around ai-coding, skill with 170 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- 美股港股全栈数据工具包 (AI Skill) — 7层架构 · 17端点 · 5数据源 · 零鉴权 US & HK Stock Full-Stack Data Toolkit for AI Coding Assistants
- Start from the README minimum path to evaluate integration effort.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating global-stock-data for the repository language AI workflows.
- Comparing a GitHub project with 170 stars and current repository activity.
Pros
- global-stock-data has visible GitHub traction with 170 stars.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
global-stock-data should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
global-stock-data depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/simonlin1212/global-stock-data.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ curl -o ~/.claude/skills/global-stock-data/SKILL.md \Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented global-stock-data example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is global-stock-data?
global-stock-data is an open-source ai coding project. 美股港股全栈数据工具包 (AI Skill) — 7层架构 · 17端点 · 5数据源 · 零鉴权 | US & HK Stock Full-Stack Data Toolkit for AI Coding Assistants
How do I install global-stock-data?
Start with the official README. The first detected setup step is: git clone https://github.com/simonlin1212/global-stock-data.git.
Is global-stock-data beginner-friendly?
If you already know the Unknown ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can global-stock-data be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does global-stock-data need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt global-stock-data?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.