by hluaguo · Agent Tool · ★ 320
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Learn FASTER AI-powered learning coach that accelerates mastery through spaced repetition, personalized syllabi, and active practice. Built for Claude Code - Integrates AI coaching directly into your development environment. Also supports Codex with agent-specific instructions and shared learning tools. Codex support is less strict than Claude Code because Codex does not currently expose a CLI flag for replacing the system prompt; Learn FASTER can only inject coaching behavior through , startup prompts, or future skill-style instructions. Codex also only exposes its structured user-input tool in Plan mode, so in Default mode it cannot present guided choice prompts the same way Claude Code can. Why Learn FASTER? Master any technical skill with science-backed learning principles: Personalized syllabi generated for your skill level and learning goals Spaced repetition system that schedules reviews at optimal intervals Four learning modes - choose Balanced, Exam-Prep, Theory-Focused, or
| Stars | 320 |
| Forks | 34 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 59.256/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-21 |
| Created | 2025-11-10 |
| Platforms | claude-code, python |
| Est. Tokens | ~11k |
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learn-faster-kit is AI-powered learning coach with spaced repetition with Claude Code - master any knowledge faster with personalized syllabi and progress tracking. It is categorized as a Agent Tool with 320 GitHub stars.
learn-faster-kit is primarily written in Python. It covers topics such as claude-code, education, learning.
You can find installation instructions and usage details in the learn-faster-kit GitHub repository at github.com/hluaguo/learn-faster-kit. The project has 320 stars and 34 forks, indicating an active community.
learn-faster-kit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to learn-faster-kit on Agent Skills Hub include antivibe, human-skill-tree, learn-anything. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.