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 Imbad0202 · Claude Skill · ★ 195
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Experiment Agent 繁體中文版 A Claude Code skill for executing, monitoring, interpreting, and verifying experiments in academic research. What It Does Runs code experiments — executes scripts (Python, R, etc.), monitors for stalls/crashes in real-time, collects results Manages human studies — plans protocols, checks IRB ethics, tracks data collection progress Interprets statistics — reads p-values, effect sizes, CIs; checks 11 types of statistical fallacies (Simpson's Paradox, survivorship bias, etc.) Verifies reproducibility — re-runs experiments and compares results Why It Exists Lu et al. (2026, Nature) demonstrated an Experiment Progress Manager for autonomous AI research. This skill brings the same execute-and-monitor capability to human-in-the-loop academic workflows — without the risks of full automation. Modes Soc
| Stars | 195 |
| Forks | 13 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 69.0067287758903/100 |
| Last Updated | 2026-09-25 |
| Created | 2026-04-09 |
| Platforms | claude-code, python |
| Est. Tokens | ~14k |
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experiment-agent is Claude Code skill for experiment execution, monitoring, statistical interpretation, and reproducibility verification. It is categorized as a Claude Skill with 195 GitHub stars.
experiment-agent is primarily written in Python.
You can find installation instructions and usage details in the experiment-agent GitHub repository at github.com/Imbad0202/experiment-agent. The project has 195 stars and 13 forks, indicating an active community.
The top alternatives to experiment-agent on Agent Skills Hub include digital-marketing-pro, life-sciences, claude-ai-mcp. 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.
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