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 joshcirre · Agent Tool · ★ 175
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is instruckt-laravel safe to install? View the security audit →
instruckt-laravel Laravel package for instruckt — visual feedback for AI coding agents. Provides the backend API, MCP tools, JSON file storage, and a Blade toolbar component. Users annotate elements in the browser, annotations are copied as structured markdown, and your AI agent can also read them via MCP. Requirements PHP 8.2+ Laravel 11 or 12 laravel/mcp (optional, for MCP tool integration) Install The install command publishes the config, installs the npm package, and automatically: Adds the instruckt Vite plugin to your with Adds to your JS entry point Configures MCP for any detected AI agents (Claude Code, Cursor, Codex, OpenCode, GitHub Copilot) To uninstall, run . See Uninstall for details. Setup Vite Plugin (Default) The install command adds the instruckt Vite plugin to your : And a single import in your J
| Stars | 175 |
| Forks | 9 |
| Language | PHP |
| Category | Agent Tool |
| Quality Score | 72.2038938595915/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-18 |
| Created | 2026-03-06 |
| Est. Tokens | ~17k |
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instruckt-laravel is A visual feedback tool for your agentic Laravel workflow.. It is categorized as a Agent Tool with 175 GitHub stars.
instruckt-laravel is primarily written in PHP.
You can find installation instructions and usage details in the instruckt-laravel GitHub repository at github.com/joshcirre/instruckt-laravel. The project has 175 stars and 9 forks, indicating an active community.
The top alternatives to instruckt-laravel on Agent Skills Hub include claude-code-skill-factory, claude-plugins, tutor-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: