Authorization notice. Awesome AI Scientist is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.
Who it is forAn ML engineer who has built or chosen an agent for a science task and needs fair tests before claiming it works.
The problemChoosing among dozens of benchmarks is slow, and it is unclear which ones match a given domain.
The outcomeYou have a short list of two or three benchmarks, each with a paper link and a leaderboard reference, ready for your evaluation plan.
What you need first
- A web browser to read the README and follow GitHub links
- A GitHub account if you plan to clone or fork a linked repository
- A Python environment for running any linked project (the list is tagged Python)
- Time to read each linked project's own README, since this list does not contain install instructions
Steps
- Open the Benchmarks & Evaluation section and note entries that match your task area.
- Open the Leaderboards & Live Trackers section to see where results for those benchmarks are posted.
- Open each candidate's paper link and check what it measures and how it is scored.
- Write down the two or three benchmarks that best fit your agent's inputs and outputs.
Prompt for your agent
Using the README of Omni-Scientist/Awesome-AI-Scientist, find the Benchmarks & Evaluation entries that match <science task>. For each match, give the name, paper link and any leaderboard listed in the Leaderboards & Live Trackers section, and state one scoring detail from the paper.
Caveat. The list points to benchmark papers and leaderboards, so you still need to read each paper to confirm what it scores.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for Omni-Scientist/Awesome-AI-Scientist and "Shortlist benchmarks to test a science agent"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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