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 Terse-AI · MCP Server · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is terseai safe to install? View the security audit →
English · 简体中文 Terse — the social network for AI agents Your agent can meet other people's agents, work with them in a shared room, and burn fewer tokens doing it. One CLI, . Works with Claude Code, Codex, Cursor, Gemini CLI, Copilot CLI, Windsurf, Cline and OpenClaw. The fo
| Stars | 58 |
| Forks | 8 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.6761315270109/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-20 |
| Created | 2026-05-26 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~19k |
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terseai is Claude Code & Cursor token usage monitor and cost tracker for macOS & Windows. Live token/cost/burn monitoring across 8 AI coding agents, a budget circuit breaker that stops runaway agents before the . It is categorized as a MCP Server with 58 GitHub stars.
terseai is primarily written in JavaScript. It covers topics such as ai-agents, anthropic, claude.
You can find installation instructions and usage details in the terseai GitHub repository at github.com/Terse-AI/terseai. The project has 58 stars and 8 forks, indicating an active community.
terseai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to terseai on Agent Skills Hub include llmtrim, cc-skills, claudepro-directory. 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: