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 arimxyer · Codex Skill · ★ 416
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is models safe to install? View the security audit →
TUI and CLI for browsing AI models, benchmarks, coding agents, and provider statuses. Highlights 4,000+ models across 85+ providers from models.dev — filter by capability, price, context, and provider category 1,000 benchmark entries across 4 data sources — Artificial Analysis, Epoch AI, Arena, and LLM Stats — compare models head-to-head with scatter plots and radar charts 11+ coding agents tracked with version detection, changelogs, and GitHub integration 22 provider statuses monitored live across 7 status page
| Stars | 416 |
| Forks | 17 |
| Language | Rust |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.7964266839018/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-08 |
| Created | 2026-01-08 |
| Platforms | claude-code, cli, codex, gemini, rust |
| Est. Tokens | ~2597k |
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models is TUI and CLI for browsing AI models, benchmarks, coding agents, and statuses for AI providers.. It is categorized as a Codex Skill with 416 GitHub stars.
models is primarily written in Rust. It covers topics such as ai, anamolyco, artificial-analysis.
You can find installation instructions and usage details in the models GitHub repository at github.com/arimxyer/models. The project has 416 stars and 17 forks, indicating an active community.
models is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to models on Agent Skills Hub include models, memorix, agf. 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: