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 freestylefly · Codex Skill · ★ 893
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wesight safe to install? View the security audit →
WeSight Desktop AI Agent Workspace for Local Coding Agents English | 简体中文 WeSight is an open-source desktop control console for local AI agents. It helps you install or reuse Claude Code, Codex, OpenClaw, Hermes Agent, OpenCode, Qwen Code, DeepSeek-TUI, and the built-in agent runtime, then gives them a visual workspace for chat, tools, files, IM channels, s
| Stars | 893 |
| Forks | 207 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 61.9129701169855/100 |
| Open Issues | 14 |
| Last Updated | 2026-08-24 |
| Created | 2026-05-07 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~18k |
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wesight is Open-source desktop AI agent workspace with one-click Claude Code, Codex, OpenClaw, Hermes Agent setup and custom LLM model routing.. It is categorized as a Codex Skill with 893 GitHub stars.
wesight is primarily written in TypeScript. It covers topics such as agent-skills, agent-workspace, ai-agent.
You can find installation instructions and usage details in the wesight GitHub repository at github.com/freestylefly/wesight. The project has 893 stars and 207 forks, indicating an active community.
wesight is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to wesight on Agent Skills Hub include token-monitor, tutti, modsearch. 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: