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 fallow-rs · Codex Skill · ★ 124
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fallow-skills safe to install? View the security audit →
Agent skills for the JavaScript and TypeScript codebase intelligence layer. Agent skills for fallow, Rust-native codebase intelligence for JavaScript and TypeScript. The free static layer reports quality, changed-code risk, cleanup opportunities, circular dependencies, code duplication, complexity hotspots, and architecture boundary violations in milliseconds. The optional paid runtime layer (Fallow Run
| Stars | 124 |
| Forks | 9 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.13377544032/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-22 |
| Created | 2026-03-20 |
| Platforms | claude-code, cli, codex, gemini, node |
| Est. Tokens | ~14k |
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fallow-skills is Agent skills for fallow, codebase intelligence for TypeScript and JavaScript. Teaches AI agents how to find unused code, duplication, circular deps, complexity hotspots, architecture drift, design-sys. It is categorized as a Codex Skill with 124 GitHub stars.
fallow-skills is primarily written in JavaScript. It covers topics such as agent-skills, amp, claude.
You can find installation instructions and usage details in the fallow-skills GitHub repository at github.com/fallow-rs/fallow-skills. The project has 124 stars and 9 forks, indicating an active community.
fallow-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fallow-skills on Agent Skills Hub include awesome-ai-agent-skills, agnix, skillnote. 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: