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 wong2 · Agent Tool · ★ 183
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is diffx safe to install? View the security audit →
diffx A local code review tool designed for the coding agent workflow. Review AI-generated changes in a GitHub PR-like web UI, leave inline comments, then hand them back to your coding agent to fix. Install Usage Run in any git repository: This starts a local server and opens your browser with a diff review UI. Options Features Split / Unified view — Toggle between side-by-side and inline diff Syntax highlighting — Powered by Shiki with GitHub themes File tree — Hierarchical file browser with search filter and file change-type icons Inline comments — Click the button on any line to add a review comment Comment replies — AI agents can reply to comments via API, displayed with bot avatar in the UI Comment status tracker — Sidebar widget showing open, replied, and resolved comment counts with click-to-navigate links Copy comments
| Stars | 183 |
| Forks | 32 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 70.2059904844614/100 |
| Open Issues | 9 |
| Last Updated | 2026-06-23 |
| Created | 2026-04-04 |
| Platforms | node |
| Est. Tokens | ~14k |
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diffx is A local code review tool designed for the coding agent workflow. It is categorized as a Agent Tool with 183 GitHub stars.
diffx is primarily written in TypeScript.
You can find installation instructions and usage details in the diffx GitHub repository at github.com/wong2/diffx. The project has 183 stars and 32 forks, indicating an active community.
The top alternatives to diffx on Agent Skills Hub include synalinks-skills, agent-skills, claude-code-skill-factory. 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: