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 MarkShawn2020 · Agent Tool · ★ 303
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is lovcode safe to install? View the security audit →
Lovcode I came, I saw, I conquered. / 我来，我见，我征服。 Desktop command table for Claude Code, Codex, terminal sessions, and local AI coding history macOS • Windows • Linux Website • Overview • Features • Docs • Installation • Usage • Development • Tech Stack • Release Notes Overview Lovcode is an open-source desktop app for developers who work with multiple AI coding tools. It brings Claude Code, Codex, terminal sessions, local chat history, linked files, runtime configuration, commands, MCP servers, skills, hooks, and MaaS providers into one local command table. The goal is simple: arrive
| Stars | 303 |
| Forks | 24 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 75.5580502659102/100 |
| Open Issues | 12 |
| Last Updated | 2026-03-31 |
| Created | 2025-12-10 |
| Platforms | claude-code, node |
| Est. Tokens | ~1516k |
These tools work well together with lovcode for enhanced workflows:
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lovcode is A desktop companion app for AI coding tools. Browse Claude Code chat history, manage configurations, commands, skills, and more.. It is categorized as a Agent Tool with 303 GitHub stars.
lovcode is primarily written in TypeScript. It covers topics such as ai-tools, claude-code, desktop-app.
You can find installation instructions and usage details in the lovcode GitHub repository at github.com/MarkShawn2020/lovcode. The project has 303 stars and 24 forks, indicating an active community.
lovcode is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to lovcode on Agent Skills Hub include yume, mcp-gearbox, repo-wizard. 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: