analytics-metrics
Build data visualization and analytics dashboards. Use when creating charts, KPI displays, metrics dashboards, or data visualization components. Triggers on…
Yuval's all-in-one AI video pipeline. Turns an idea/script into a finished, on-brand MP4 by orchestrating HyperFrames (HTML→deterministic video render), Lottie (branded motion graphics), ManimCE (math / neural-network / concept animations), and a transcribe→approve caption flow
$ npx -y skills add hoodini/ai-agents-skills --skill yuv-video-director --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/yuv-video-directorContext preview
The summary Claude sees to decide when to auto-load this skill.
Yuval's all-in-one AI video pipeline. Turns an idea/script into a finished, on-brand MP4 by orchestrating HyperFrames (HTML→deterministic video render), Lottie (branded motion graphics), ManimCE (math / neural-network / concept animations), and a transcribe→approve caption flow
name: yuv-video-director description: > Yuval's all-in-one AI video pipeline. Turns an idea/script into a finished, on-brand MP4 by orchestrating HyperFrames (HTML→deterministic video render), Lottie (branded motion graphics), ManimCE (math / neural-network / concept animations), and a transcribe→approve caption flow — all wrapped in the YUV.AI Neon Phoenix brand via a frame.md. Use whenever Yuval wants to make, edit, or explain something as a video: promo, explainer, launch, social reel, "make a video about X", "explain X as a video", "neural network animation", "turn this into a video", captioned tutorial, 16:9 or 9:16. Triggers: video, explainer, promo, reel, manim, lottie, hyperframes, animation, "make a video", "explain ... as a video", מצגת וידאו, סרטון, הסבר וידאו. Routes each beat to the right engine, wraps in brand, self-verifies, and renders.
The conductor for YUV.AI video. You (the agent) decide **what each beat needs**, route it to the right **engine**, compose everything into **one HyperFrames composition**, wrap it in the **YUV.AI Neon Phoenix** brand, **self-verify**, and **render** to MP4. This skill is the router + the working reference implementations; load a reference file only when that engine is in play.
> **Design source of truth:** the `yuv-design-system` skill (Neon mode — pink `#FF1464`, cyan > `#00E5FF`, rich-black/white, Anton+Inter+JetBrains Mono, neural-net phoenix motif). The video > form of it is **`frame.md`** — see [references/frame-md.md](references/frame-md.md). Bundled > template: [assets/FRAME.md](assets/FRAME.md). Drop it in the project root; HyperFrames reads it.
HyperFrames renders by **seeking each frame in headless Chrome → FFmpeg** (`frameIndex = floor(t·fps)`, same input → same output). So every visual is one of two kinds:
| Pattern | Runs… | Engines | Rule | |---|---|---|---| | **Live seekable adapter** | *inside* the render, driven to time `t` per frame | GSAP, Lottie (`window.__hfLottie`), Three.js, a canvas driven by a GSAP proxy `onUpdate` | must be **clock-driven** — no `Date.now()`, `Math.random()` (seed a mulberry32), `setTimeout`, or `.play()` | | **Pre-rendered asset** | *offline*, outputs a file, imported as a clip | **ManimCE** (Python→MP4/alpha), TTS audio, background-removal | render first, then drop in as a `<video>`/asset clip |
**If it can be seeked, it's an adapter. If it can't, pre-render it.** Manim is *always* a pre-rendered clip — it has its own renderer and runs in Python; it can never be a live adapter.
"explain X as a TEASER/promo (FOMO, cliffhanger, fast)" → references/teaser-explainer.md (the formula) "explain a concept / math / neural network / algorithm / training" → ManimCE (pre-rendered clip — cut into BURSTS for teasers) "branded motion: logo sting · stat reveal · icon pop · pulse" → Lottie (live, lottie-web) "kinetic captions · titles · reveals · transitions · data callouts" → GSAP (live) ← default "3D / spatial" → Three.js (live) (Babylon NOT used) speech → captions → transcribe + approve webapp (see video-edit skill) no voiceover provided → TTS (Kokoro: npx hyperframes tts) brand colors / fonts / motifs → frame.md (picked up front)
GSAP is the reliable default for text/motion. Reach for Lottie for *designed* branded graphics, Manim for *explaining* an idea.
1. **Plan the beats.** Narrative arc + which engine each beat needs. Pick 16:9 and/or 9:16. 2. **Set the brand.** Ensure `FRAME.md` is in the project root (copy [assets/FRAME.md](assets/FRAME.md)). All colors/fonts/motifs come from it — never invent. **Three brand must-haves on every video** (see [references/brand-kit.md](references/brand-kit.md) + [references/cinematic.md](references/cinematic.md)): the **real phoenix logo** ([assets/logo-phoenix.png](assets/logo-phoenix.png)) at the reveal + end card, a **real featured Lottie** (generate one — [assets/lottie-burst-generator.py](assets/lottie-burst-generator.py)), and the **link end-card** (logo + "LET'S FLY HIGH" + the full link set + CTA). For teaser/social pacing see [references/editing.md](references/editing.md); for psychological/cliffhanger/FOMO cuts see [references/cinematic.md](references/cinematic.md). 3. **Pre-render the asset beats first** (so they exist as clips):
4. **Scaffold + compose.** `npx hyperframes init <slug> --non-interactive`. Author `index.html` (the `hyperframes` skill is the contract). Use:
5. **Self-verify (gates).** [references/gates.md](references/gates.md): `npx hyperframes lint` (0 errors) → `validate` (0 console errors, WCAG AA) → render → spot-check 5 frames across the timeline. Fix → re-run. Lottie MUST be screenshot-verified (the Skottie-vs-lottie-we
🧠 AI Agent Skills Repository - A curated collection of specialized skills for AI coding agents (Claude Code, GitHub Copilot, Cursor, Windsurf). Created by Yuval Avidani using GitHub Copilot via VS Code Insiders.
Repo: hoodini/ai-agents-skills
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