TenureAI

TenureAI / PhD-Zero

Public

Autoresearch with PhD-level workflows and modular agent skills. Built for the autonomous AI Scientist.

agent ai-scientist auto-research autoresearch claude-code
39
2
100% credibility
Found Mar 12, 2026 at 36 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

PhD-Zero is a library of reusable skills and workflows designed to help AI agents manage structured research processes from planning and deep investigation to experiment execution and paper drafting.

How It Works

1
🔍 Discover PhD-Zero

You stumble upon PhD-Zero, a handy collection of skills that turns everyday AI helpers into smart research partners capable of planning, digging deep, running tests, and writing up findings.

2
📥 Grab the skill pack

You download the simple skill library to your computer, making all these research tools ready to explore at your own pace.

3
📖 Explore the guides

You read the friendly instructions and see the lineup of skills like making plans, gathering info, and organizing memories.

4
⭐ Pick your research tools

You choose the perfect skills for your project, feeling excited as your AI helper gains pro-level abilities for deep thinking and experiments.

5
🔗 Share skills with your AI

You easily connect these skills to your AI assistant, so it knows exactly how to handle research steps smoothly.

6
🚀 Launch a research project

You give your AI a big research goal, and it plans, researches, tests ideas, checks with you when needed, and keeps everything on track.

✅ Celebrate your results

Your AI delivers a polished research output with clear plans, evidence, experiments, and even a draft write-up, saving you tons of time and effort.

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AI-Generated Review

What is PhD-Zero?

PhD-Zero delivers modular Python skills for autonomous agents tackling PhD-level autoresearch workflows, turning vague goals into structured plans, deep literature dives, experiment runs, memory tracking, human checkpoints, and paper drafts. Built as an operating system for research coding agents, it enforces discipline on long tasks—planning evidence, executing code, and producing inspectable outputs—rather than one-off clever responses. Users get a shared library compatible with OpenAI and Claude runtimes for consistent, reusable agent behaviors.

Why is it gaining traction?

It stands out by prioritizing workflow modularity over giant prompts, letting agents chain skills like research planning and experiment execution across projects without reinventing processes. Developers hook on the quick validation via git clone and directory checks, plus CI-synced mirrors for multiple agent types, making it easy to plug into existing setups. The focus on reproducibility and human oversight appeals to those burned by hallucinating agents in real R&D.

Who should use this?

AI researchers building autonomous scientists for PhD-level tasks like net zero PhD modeling, zero knowledge proof PhD validations, or zero vigyan PhD admissions analysis. Teams in autoresearch needing modular skills for experiment execution and paper writing in Python agent pipelines. Suited for devs handling zero cutoff for PhD benchmarks or zero MS MD PhD comparisons, where workflow discipline trumps raw speed.

Verdict

With 35 stars and a 1.0% credibility score, PhD-Zero is raw and early—light docs, no heavy tests—but its tight skill set shows real promise for agent builders. Clone it if you're prototyping PhD workflows; skip for production until contributions flesh it out.

(198 words)

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