generative-ai-guide
Curated guide to generative AI covering LLMs and diffusion models
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill generative-ai-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Generative AI Guide A skill providing a comprehensive, curated guide to generative AI research and practice, covering large language models (LLMs), diffusion models, transformer architectures, prompt engineering, and evaluation methodologies. Based on the awesome-generative-ai-guide repository (25K stars), this skill equips researchers with structured knowledge of the rapidly evolving generative AI landscape. ## Overview Generative AI has become one of the most active areas of research across computer science, with implications spanning natural language processing, computer vision, audio synthesis, code generation, scientific discovery, and creative applications. The pace of development makes it challenging for researchers to maintain a current understanding of the field. This skill provides a structured map of the generative AI landscape, organized by topic and application area, with guidance on key papers, methods, and practical considerations. Whether you are an AI researcher staying current with the field, a domain scientist exploring how generative AI can accelerate your work, or a student entering the field, this skill provides the orientation and resources needed to naviga
- Overview
- Large Language Models
- Diffusion Models
- Prompt Engineering
- Evaluation and Benchmarks
- Integration with Research-Claw
- Best Practices
What does the generative-ai-guide skill do?
Curated guide to generative AI covering LLMs and diffusion models
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill generative-ai-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.