No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by K-Dense-AI · Agent Tool · ★ 331
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is science-superpowers safe to install? View the security audit →
Science Superpowers Science Superpowers is a complete computational-science methodology for your research agents, built on a set of composable skills plus initial instructions that make sure your agent actually uses them. It has zero third-party dependencies — it runs with only your agent harness and a POSIX shell. ⭐ If Science Superpowers helps your research, please star this repository. A star helps other scientists and engineers find the project and tells us the methodology is worth expanding. Learn more: Introducing Science Superpowers — why we built it, the Iron Law, and the full workflow. Stay up to date: Follow K-Dense on X, LinkedIn, and YouTube for new skills, release announcements, a
| Stars | 331 |
| Forks | 32 |
| Language | Shell |
| Category | Agent Tool |
| Quality Score | 63.9812707104696/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-13 |
| Created | 2026-05-28 |
| Platforms | cli |
| Est. Tokens | ~13k |
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science-superpowers is Composable computational-science methodology skills for AI research agents — pre-registration over TDD. A science-domain reimplementation of Superpowers.. It is categorized as a Agent Tool with 331 GitHub stars.
science-superpowers is primarily written in Shell. It covers topics such as agent-skills, ai-agents, computational-science.
You can find installation instructions and usage details in the science-superpowers GitHub repository at github.com/K-Dense-AI/science-superpowers. The project has 331 stars and 32 forks, indicating an active community.
The top alternatives to science-superpowers on Agent Skills Hub include Auto-Research-Skills, claude-workflow-v2, Awesome-Agent-Skills-for-Empirical-Research. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: