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 kaanozhan · Codex Skill · ★ 364
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Frame safe to install? View the security audit →
Frame The platform for agentic development. Frame started as a lightweight terminal-first IDE. It's evolving into a full platform for developing and managing larger projects with AI agents — bringing structure, context, and organization to the way you work with Claude Code, Codex CLI, and Gemini CLI. https://github.com/user-att
| Stars | 364 |
| Forks | 36 |
| Language | JavaScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 71.3834100954764/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-20 |
| Created | 2026-01-20 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~16k |
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Frame is ADE( Agentic Development Environment) The spec-driven environment for AI coding agents, where your planning becomes lasting, shared project context.. It is categorized as a Codex Skill with 364 GitHub stars.
Frame is primarily written in JavaScript. It covers topics such as ade, agentharness, agentic.
You can find installation instructions and usage details in the Frame GitHub repository at github.com/kaanozhan/Frame. The project has 364 stars and 36 forks, indicating an active community.
Frame is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Frame on Agent Skills Hub include happier, spec-kitty, agtx. 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: