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 templetongroup · Agent Tool · ★ 102
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is radiant safe to install? View the security audit →
100% free and open source. MIT licensed — use it, modify it, redistribute it, sell it. A local coding harness for your Mac: chat with coding agents across cloud and local models, watch them work in a live activity feed, and drive a real terminal — all in one window. An agent working in a real folder — every file it touched is listed, and the composer shows which model is answering and what it is allowed to do. Longer jobs run as tasks. "Needs you" is the column that matters: an agent that hits a decision stops there instead of guessing. A loop is a
| Stars | 102 |
| Forks | 19 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.7585555883917/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-08-19 |
| Platforms | claude-code, node |
| Est. Tokens | ~14k |
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radiant is A local coding harness for Mac. Chat with coding agents across cloud and local models, watch every tool call in a live activity feed, and drive a real terminal — in one window. Free and MIT licensed.. It is categorized as a Agent Tool with 102 GitHub stars.
radiant is primarily written in JavaScript. It covers topics such as ai, ai-agents, app.
You can find installation instructions and usage details in the radiant GitHub repository at github.com/templetongroup/radiant. The project has 102 stars and 19 forks, indicating an active community.
radiant is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to radiant on Agent Skills Hub include palot, c4-genai-suite, CoWork-OS. 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: