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 saffron-health · Codex Skill · ★ 877
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is libretto safe to install? View the security audit →
Libretto Libretto is a toolkit for building robust web integrations. It gives your coding agent a live browser and a token-efficient CLI to: Inspect live pages with minimal context overhead Capture network traffic to reverse-engineer site APIs Record user actions and replay them as automation scripts Debug broken workflows interactively against the real site We at Saffron Health built Libretto to help us maintain our browser integrations to common healthcare software. We're open-sourcing it so other teams have an easier time doing the same thing. https://github.com/user-attachments/assets/9b9a0ab3-5133-4b20-b3be-459943349d18 Quick Links Website: libretto.sh Docs: libretto.sh/docs Repository: [github.c
| Stars | 877 |
| Forks | 68 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 73.6492626092585/100 |
| Open Issues | 17 |
| Last Updated | 2026-08-21 |
| Created | 2026-03-02 |
| Platforms | browser, claude-code, codex, node |
| Est. Tokens | ~15k |
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libretto is The AI toolkit for building reliable browser automations. It is categorized as a Codex Skill with 877 GitHub stars.
libretto is primarily written in TypeScript. It covers topics such as automation, browser, claude-code.
You can find installation instructions and usage details in the libretto GitHub repository at github.com/saffron-health/libretto. The project has 877 stars and 68 forks, indicating an active community.
libretto is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to libretto on Agent Skills Hub include Overture, cocoindex-code, claude-code-plugins-plus-skills. 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: