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 JohnHeibel · AI Tool · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PDoomVideo safe to install? View the security audit →
Featured in: Video Skills for Claude Code & Opus 5.5
I'm Upping My P(doom) Source code for the Claude Opus 5.5 music video for I'm Upping My P(doom). I've also made a new more reliable and general base for making Claude animations with Claude Opus 5.5. Highly recommend checking it out! Credits Inspired by: this post on X Song: as far as I could find, it comes from this YouTube video from 2024 Lyrics from Osmarks showing the original udio generations and the interpretation of each lyricsongobjectivelycorrectinterpretation) How it was made The video took two generations, both in Claude Code: First generation (): Claude Opus 5.5 (Medium) Second generation (everything else): Claude Opus 5.5 Everything in this repository was generated by the model. No scene ideas were specified. The only direction was to: use the Clawd character design give each lyric interesting visuals and transitions was written by Opus
| Stars | 1,138 |
| Forks | 119 |
| Language | JavaScript |
| Category | AI Tool |
| Quality Score | 52.8316774976426/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-25 |
| Created | 2026-09-22 |
| Platforms | claude-code, node |
| Est. Tokens | ~13k |
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PDoomVideo is Source code for the Claude Opus 5.5 music video for I'm Upping My P(doom). It is categorized as a AI Tool with 1.1k GitHub stars.
PDoomVideo is primarily written in JavaScript.
You can find installation instructions and usage details in the PDoomVideo GitHub repository at github.com/JohnHeibel/PDoomVideo. The project has 1.1k stars and 119 forks, indicating an active community.
The top alternatives to PDoomVideo on Agent Skills Hub include vision-agent, axton-obsidian-visual-skills, claudekit-skills. 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: