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 U-C4N · Claude Skill · ★ 297
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is U-Pool safe to install? View the security audit →
U-Pool Provider switcher for Claude Code and Codex. Keep every endpoint, API key, and model in one place. Switch the active provider with a click. Probe reachability before you start working. Python backend · Next.js UI · native OS webview — no Electron, no Node at runtime. Screenshot Why U-Pool? Switching between Anthropic, OpenRouter, DeepSeek, Azure, xAI, and custom relays usually means editing config files by hand. U-Pool turns that into a desktop app: One list of providers for Claude Code and Codex One click to make a provider live — in the config file and in the Windows environment the CLIs read Health checks that measure latency without spending tokens Atomic writes, and a backup beside every file U-Pool c
| Stars | 297 |
| Forks | 3 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 64.6671066730939/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-07 |
| Created | 2026-07-25 |
| Platforms | browser, claude-code, cli, codex, python |
| Est. Tokens | ~16k |
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U-Pool is Desktop provider switcher for Claude Code and Codex. Keep every endpoint, API key, and model in one place, switch the active provider in one click, and probe latency before you start. Atomic writes an. It is categorized as a Claude Skill with 297 GitHub stars.
U-Pool is primarily written in Python. It covers topics such as ai-tools, api-key-management, claude-code.
You can find installation instructions and usage details in the U-Pool GitHub repository at github.com/U-C4N/U-Pool. The project has 297 stars and 3 forks, indicating an active community.
The top alternatives to U-Pool on Agent Skills Hub include LoongPort, code-on-incus, Overture. 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: