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 Fanzzzd · Agent Tool · ★ 336
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is repo-wizard safe to install? View the security audit →
Repo Wizard Read in Chinese (中文) [!NOTE] This project is under active development. Issues and PRs are welcome! Download You can download the latest version for macOS, Windows, and Linux from the GitHub releases page. Tired of the tedious and error-prone copy-paste dance when applying AI-generated code changes? Rep
| Stars | 336 |
| Forks | 6 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 63.0167246380602/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-03 |
| Created | 2025-06-16 |
| Platforms | node |
| Est. Tokens | ~572k |
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repo-wizard is A professional tool to safely and efficiently apply LLM-suggested code changes to your local codebase in a controllable, reviewable way.. It is categorized as a Agent Tool with 336 GitHub stars.
repo-wizard is primarily written in TypeScript. It covers topics such as code-generator, desktop-app, developer-tools.
You can find installation instructions and usage details in the repo-wizard GitHub repository at github.com/Fanzzzd/repo-wizard. The project has 336 stars and 6 forks, indicating an active community.
repo-wizard is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to repo-wizard on Agent Skills Hub include yume, tabularis, lovcode. 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: