by lixiang0618 · Agent Tool · ★ 125
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
LearnGo, a ios versatile learning tool with RAG and Prompting under LLM like GPT-4o-mini, enabling subject Q&A, smart note generation from uploaded materials, offline photo(or AR) object recognition with translations, and multilingual voice input (Cantonese, English, Mandarin). It also features customizable themes for a personalized experience
| Stars | 125 |
| Forks | 2 |
| Language | Swift |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 56.6531151109774/100 |
| Last Updated | 2024-11-07 |
| Created | 2024-10-20 |
| Est. Tokens | ~6719k |
Looking for a LearnGo alternative? If you're comparing LearnGo with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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LearnGo is LearnGo, a ios versatile learning tool with RAG and Prompting under LLM like GPT-4o-mini, enabling subject Q&A, smart note generation from uploaded materials, offline photo(or AR) object recognition w. It is categorized as a Agent Tool with 125 GitHub stars.
LearnGo is primarily written in Swift. It covers topics such as app, ios, llm.
You can find installation instructions and usage details in the LearnGo GitHub repository at github.com/lixiang0618/LearnGo. The project has 125 stars and 2 forks, indicating an active community.
LearnGo is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LearnGo on Agent Skills Hub include ai-review, LLM-Agents-Ecosystem-Handbook, aser. 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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