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by povio · Agent Tool · ★ 58
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Lingua Lingua is a tool designed to streamline the localization process for iOS and Android platforms. It provides a unified solution for generating localization files from a single source, Google Sheets. There are several benefits of using the tool: Unified translation system for iOS and Android platforms Seamless integration with project workflows for automatic translation imports Minimized risk of typo errors in localization usage Enhanced productivity through intelligent auto-completion and semantic clarity Safeguard against referencing non-existent translations Setup Google Sheet In order to setup Google sheet, we need to complete two steps. First one is to create a sheet with predefined structu
| Stars | 58 |
| Forks | 5 |
| Language | Swift |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72.5040997324199/100 |
| Last Updated | 2026-05-26 |
| Created | 2023-05-03 |
| Est. Tokens | ~100k |
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Lingua is Agentic localization. A unified localization management tool for iOS & Android. It is categorized as a Agent Tool with 58 GitHub stars.
Lingua is primarily written in Swift. It covers topics such as ai-agents, android, ios.
You can find installation instructions and usage details in the Lingua GitHub repository at github.com/povio/Lingua. The project has 58 stars and 5 forks, indicating an active community.
Lingua is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Lingua on Agent Skills Hub include ios-marketing-capture, deepcontext-mcp, phone-mcp. 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.
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