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 makethisbetter · MCP Server · ★ 90
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is makethisbetter-js safe to install? View the security audit →
makethisbetter Drop-in widget for AI-powered user feedback and automated fixes. makethisbetter.dev · Why You ship with AI agents. Your users hit bugs you never see in dev. They leave. You never find out why. This widget gives your users a way to report exactly what went wrong — annotated screenshot, console errors, DOM state, browser info — in two clicks. AI triage turns that into a structured task your coding agent (Claude Code, Cursor, Codex) picks up automatically. The agent ships the fix. The user gets notified. No more "can you describe what happened?" No more lost screenshots in Slack. The full loop, from frustrated user to shipped
| Stars | 90 |
| Forks | 13 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.13377544032/100 |
| Last Updated | 2026-09-10 |
| Created | 2026-06-28 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~18k |
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makethisbetter-js is Feedback widget with screenshots, annotations, and AI triage. It is categorized as a MCP Server with 90 GitHub stars.
makethisbetter-js is primarily written in TypeScript. It covers topics such as ai, claude-code, cursor.
You can find installation instructions and usage details in the makethisbetter-js GitHub repository at github.com/makethisbetter/makethisbetter-js. The project has 90 stars and 13 forks, indicating an active community.
makethisbetter-js is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to makethisbetter-js on Agent Skills Hub include ue5-mcp, agnix, Context-Engine. 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: