Agent Skills

Abstract

1. Introduction

For operators, technical leads, and business owners who need to verify a root cause before choosing a fix.

A collection of method-driven skills for people who use AI to make decisions, run projects, analyze evidence, create documents, and produce finished work. These skills are designed for active collaborators: you provide the real context and remain responsible for the decision; the agent supplies structure, analysis, and quality checks.

Most agent tools use slash commands. Codex and ChatGPT use $; in those tools, replace the leading / with $.

2. Objective

Method-driven agent skills for knowledge work: strategy, research, critical thinking, data analysis, docs, slides. Works with Claude Code, Codex, Cursor and any agentskills.io-compatible agent.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/tronghieu/agent-skills.git
cd agent-skills

1. Project scope: .agents/skills/ or .claude/skills/
2. User scope on macOS/Linux: ~/.agents/skills/ or ~/.claude/skills/
3. User scope on Windows: %USERPROFILE%\.agents\skills\ or %USERPROFILE%\.claude\skills\

npx skills add tronghieu/agent-skills

/plugin marketplace add tronghieu/agent-skills
/plugin install agent-skills@tronghieu-skills

/market-researcher Assess the market for this product idea in Vietnam.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by tronghieu and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.