Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI research and engineering teams working on agentic scientific workflows: google-deepmind/science-skills is the official Google DeepMind collection of 30+ agent skills covering genomics, structural biology, cheminformatics, literature search, and clinical / pharmacological / regulatory databases, with every skill grounded in a primary scientific database (AlphaGenome, AFDB, UniProt, Pu
Who should use it
Who should skip it
Move on from google-deepmind/science-skills if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
google-deepmind/science-skills is tracked by RepoRadar as a official google deepmind agent s in the Apache-2.0 official Google DeepMind collection o section. It was first seen on 2026-06-25 and last updated on 2026-06-25. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for google-deepmind/science-skills are workflow potential (9.5) and maturity (9.1), while setup ease (6.4) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned google-deepmind/science-skills a composite score of 8.4 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 2059.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
**Some skills require API keys.** The README explicitly notes 'Some skills, such as AlphaGenome and OpenAlex, require an API key to function' — users must obtain the relevant API key before triggering those skills, and the API key has to be configured in the agent's environment or skill-specific config file.; **First-time install requires `uv` package manager and approval prompt.** The README says 'The first time you trigger a Science Skill, the agent will ask for approval and install `uv`, and then proceed to respond to your scientific query / task. We recommend restarting Antigravity after this first time installation.' — users should expect the first invocation to trigger a package-manager install and an approval prompt.; **Primary databases can change schemas, rate limits, or terms of service.** The skills are grounded in 30+ primary scientific databases (AlphaGenome, AFDB, UniProt, PubMed, ChEMBL, OpenAlex, PDB, etc.), and any of those databases can change their API schema, rate limits, or terms of service without notice; users running the skills in production should pin to a tested commit and monitor the upstream database status pages..