zechenzhangAGI/AI-research-SKILLs
Open-source skills library for autonomous AI research agents.
View on GitHub Updated 2026-06-16
What is zechenzhangAGI/AI-research-SKILLs?
The AI Research Skills Library is a comprehensive open-source collection designed to let AI agents independently manage complex research tasks. It includes both high-level orchestration tools and deep technical skills across 23 categories such as model architecture, fine-tuning, safety, and multimodal systems.
Agents interact with the library through structured documentation and code examples drawn from official sources and real-world deployments. The autoresearch component coordinates workflows by routing tasks to the appropriate domain skills as needed.
It targets AI researchers, agent developers, and teams building autonomous systems who want reliable, expert-level guidance without reinventing infrastructure or debugging setups repeatedly.
What you can build with zechenzhangAGI/AI-research-SKILLs
End-to-end paper generation
An agent surveys literature, forms hypotheses, runs experiments with distributed training skills, and produces a formatted research paper.
Model optimization projects
Agents apply post-training, quantization, and inference skills to improve existing models for specific hardware or latency targets.
Safety and alignment audits
The library's dedicated safety skills let agents systematically evaluate models for risks and implement alignment techniques.
Install zechenzhangAGI/AI-research-SKILLs
Install
npx @orchestra-research/ai-research-skillsQuick start
npx @orchestra-research/ai-research-skills- 1Run the interactive installer with npx @orchestra-research/ai-research-skills
- 2Select your coding agent from the auto-detected options (Claude, Cursor, etc.)
- 3Skills are installed to ~/.orchestra/skills/ with appropriate symlinks
- 4Point your agent to the welcome documentation to load the autoresearch layer
- 5Begin a research task and let the agent orchestrate the required skills
zechenzhangAGI/AI-research-SKILLs: pros & cons
Pros
- +Broad coverage of 98 skills across the entire research pipeline
- +Production-ready examples and troubleshooting drawn from real workflows
- +Orchestration layer that coordinates multiple specialized skills automatically
- +MIT licensed and actively maintained with community support channels
Cons
- –Primarily targets users already working with specific agent frameworks
- –Installation process assumes familiarity with terminal and agent tooling
- –Heavy focus on engineering skills may require additional domain knowledge for novel research
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Frequently asked questions
It auto-detects and integrates with Claude Code, Cursor, Gemini CLI, and several other popular coding agents.
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