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 tingly-dev · Claude Skill · ★ 347
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tingly-box safe to install? View the security audit →
Announcement: Here is fault record. Please update to the latest version to resolve known issues. Thank you for your continued support. Tingly Box Quick Start • Features • Integration • Documentation • Issues Tingly Box serves agents, coordinates AI models, optimizes context, and routes requests for maximum efficiency — with built-in remote control and secure, customizable integrations. Key Features Agent-First Model Gateway – Unified endpoint for agents — seamlessly bridge OpenAI, Anthropic, Google Gemini, and more with automatic protocol translation and native agent compatibility Agent Integration – One-click config for Claude Code, OpenCode, Codex, Xcode, and more — transparent proxying for SDKs and CLI tools Agent Profiles
| Stars | 347 |
| Forks | 42 |
| Language | Go |
| Category | Claude Skill |
| License | MPL-2.0 |
| Quality Score | 71.3569087505159/100 |
| Open Issues | 79 |
| Last Updated | 2026-09-22 |
| Created | 2025-11-21 |
| Platforms | claude-code, go |
| Est. Tokens | ~18k |
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tingly-box is Your Intelligence, Orchestrated. Every builder. Every team. Every agent. For Everyone.. It is categorized as a Claude Skill with 347 GitHub stars.
tingly-box is primarily written in Go. It covers topics such as claude-code, dsh, dsh-plugin.
You can find installation instructions and usage details in the tingly-box GitHub repository at github.com/tingly-dev/tingly-box. The project has 347 stars and 42 forks, indicating an active community.
tingly-box is released under the MPL-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to tingly-box on Agent Skills Hub include wesight, modsearch, cetus. 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: