Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by velmighty · Claude Skill · ★ 55
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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youtube-to-knowledge Turn any YouTube video into a transcript, summary, and knowledge graph — powered by Claude Code. Paste a link. Get structured knowledge. How it works Fetches the transcript (YouTube API or local Whisper) Generates a structured summary in the video's language Extracts entities and relationships → builds an interactive knowledge graph Requirements Python 3.10+ Claude Code ffmpeg (required for Whisper mode) Install ffmpeg Setup Open the folder in Claude Code. Usage Process multiple videos at once: Process an entire playlist: Already-processed videos are skipped automatically. Additional commands: — deep-dive questions about what was said in a processed video — explore entity connections across videos Output Files are saved to : raw/ transcript.txt raw transcript metadata<
| Stars | 55 |
| Forks | 9 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 75.8872293417801/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-16 |
| Created | 2026-04-06 |
| Platforms | claude-code, python |
| Est. Tokens | ~4k |
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youtube-to-knowledge is Turn any YouTube video into a transcript, summary, and knowledge graph - powered by Claude Code.. It is categorized as a Claude Skill with 55 GitHub stars.
youtube-to-knowledge is primarily written in Python. It covers topics such as claude-code, knowledge-graph, learning-tools.
You can find installation instructions and usage details in the youtube-to-knowledge GitHub repository at github.com/velmighty/youtube-to-knowledge. The project has 55 stars and 9 forks, indicating an active community.
youtube-to-knowledge is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to youtube-to-knowledge on Agent Skills Hub include mcp-video-analyzer, ytfetcher, AutoRedTeam-Orchestrator. 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: