by razr001 · Codex Skill · ★ 320
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🔒 Is align-dev safe to install? View the security audit →
AlignDev Frontend conventions generator for the AI coding era · Generate team standards and SKILL.md in one flow Build with AI. Ship with standards. A 7-step visual wizard that generates a complete Markdown standards document and a that Claude Code, Cursor, GitHub Copilot, and other coding agents can read directly. 📖 About AlignDev AlignDev is a frontend conventions generator built for the AI coding era. When teams use Claude Code, Cursor, Copilot, Windsurf, and other AI agents at the same time, the biggest pain point is consistency: each agent writes code based on its own interpretation, so directory structure, naming, state management, and UI
| Stars | 320 |
| Forks | 4 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 59.7330179037347/100 |
| Last Updated | 2026-06-05 |
| Created | 2026-06-03 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~13k |
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align-dev is AlignDev helps AI-assisted frontend teams generate shared coding standards and SKILL.md so Claude Code, Codex, Cursor, Copilot, and other agents write consistently.. It is categorized as a Codex Skill with 320 GitHub stars.
align-dev is primarily written in TypeScript. It covers topics such as ai, claude-code, codex.
You can find installation instructions and usage details in the align-dev GitHub repository at github.com/razr001/align-dev. The project has 320 stars and 4 forks, indicating an active community.
The top alternatives to align-dev on Agent Skills Hub include getspecstory, agnix, ccmanager. 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.
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