Claude Marketplaces
SkillsMCP serversMarketplacesDigestTools
Submit

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP servers by category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control
Claude Marketplaces

An independent directory of skills, plugins and MCP servers for Claude Code. Not affiliated with Anthropic.

Browse

  • Skills
  • MCP servers
  • Marketplaces
  • Weekly digest
  • Skill index
  • MCP index
  • Marketplace index

Site

  • About
  • Tools
  • Submit
  • Feedback
  • Privacy
  • llms.txt
  • Plugins reference

Maker

  • Snug
  • Agent Caffeine
  • knobs
  • Wax
  • Vectoys
  • mertduzgun.com
Crafted at
Skills/AI & Agent Building
hoodini avatar

Yuv Video Director

hoodini/ai-agents-skills

Yuval's personal video factory that routes each beat of an idea to the right rendering engine and wraps it in the YUV.AI Neon Phoenix brand. It conducts HyperFrames (HTML to deterministic video), Lottie motion graphics, and ManimCE math animations into a single MP4. The architecture is clever: anything seekable runs live inside the render loop, anything that can't be seeked gets pre-rendered as a clip. You get branded templates, a neural net canvas background, six production Lotties, and a self-verification gate system that lints before render. Built for making explainers, promos, and social reels when you need more than screen recording but less than After Effects.

GitHub stars
225
last updated
Jun 10, 2026
View on GitHubSave

Install

$ npx -y skills add hoodini/ai-agents-skills --skill yuv-video-director --agent claude-code

Installs into .claude/skills of the current project.

Files

yuv-video-director

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. Bundled template: assets/FRAME.md. Drop it in the project root; HyperFrames reads it.

The one law (decides everything)

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:

PatternRuns…EnginesRule
Live seekable adapterinside the render, driven to time t per frameGSAP, Lottie (window.__hfLottie), Three.js, a canvas driven by a GSAP proxy onUpdatemust be clock-driven — no Date.now(), Math.random() (seed a mulberry32), setTimeout, or .play()
Pre-rendered assetoffline, outputs a file, imported as a clipManimCE (Python→MP4/alpha), TTS audio, background-removalrender 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.

Route each beat to an engine

"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.

Workflow

  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). All colors/fonts/motifs come from it — never invent. Three brand must-haves on every video (see references/brand-kit.md + references/cinematic.md): the real phoenix logo (assets/logo-phoenix.png) at the reveal + end card, a real featured Lottie (generate one — 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; for psychological/cliffhanger/FOMO cuts see references/cinematic.md.
  3. Pre-render the asset beats first (so they exist as clips):
    • Manim → references/manim.md + assets/manim-scene-template.py. Render py -m manim render -qh --fps 30 scene.py SceneName, copy the MP4 into assets/.
    • Captions/TTS → reuse the video-edit skill (transcribe → approve webapp → sync) and npx hyperframes tts.
  4. Scaffold + compose. npx hyperframes init <slug> --non-interactive. Author index.html (the hyperframes skill is the contract). Use:
    • the seekable neural-net field background → assets/neural-net-field.js
    • Lottie the brand way → references/lottie.md + assets/neural-pulse.json
    • the Manim render as a <video class="clip" muted playsinline> body clip
    • GSAP entrances per scene, a neon flash transition on each boundary, fade-out only on the final scene
    • for teaser / social pacing (fast hard cuts, kinetic word-slams, a hook montage, glitch/chromatic stabs) → references/editing.md — cut it like a producer, not a slideshow
  5. Self-verify (gates). 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-web trap).
  6. Render. npx hyperframes render --fps 30 --output renders/<name>_FINAL.mp4. For vertical, clone with a 1080×1920 layout (see video-edit).

Prerequisites (check first; degrade gracefully)

See references/prereqs.md. Need Node 22+, FFmpeg, Python 3.11+ with pip (Manim/captions). On this machine: real Python is py → C:\Python313 (the bare python is Hermes' venv with NO pip — don't use it). ManimCE installs via py -m pip install manim (no LaTeX needed if you author with Text()/MarkupText, not Tex/MathTex). If Manim isn't available → skip math beats or offer to install; never hard-fail the whole video.

What this skill bundles (reference implementations that lint + render)

FileWhat it is
assets/FRAME.mdYUV.AI Neon Phoenix video frame spec (rebranded from HeyGen's Coral pack)
assets/neural-net-field.jsDeterministic, seekable neural-net phoenix canvas background
assets/neural-pulse.jsonHand-authored Bodymovin Lottie (renders in lottie-web, not just Skottie)
assets/gen_content_lotties.pyGenerator for 6 production-grade CONTENT lotties (WhatsApp-collapse, decode-beam, eye-read, ghost-line hero, orb-extract, brain-fire) — transparent, persistent+continuous, lottie-web verified
assets/{wa-collapse,decode-beam,eye-read,ghost-line,orb-extract,brain-fire}.jsonThe 6 generated content lotties, ready to drop in
assets/manim-scene-template.pyManimCE scene template, neon brand styling, Text-only (no LaTeX)
assets/what_is_nn.py"What is a neural network" ManimCE scene (neuron → network → training, 34.5s)
assets/Anton-Regular.woff2Local Anton (renderer doesn't auto-resolve it; declare @font-face)
references/composition-pattern.mdThe full multi-engine index.html pattern (field + Lottie + Manim clip + GSAP + flash)
references/teaser-explainer.mdThe cinematic teaser-explainer formula: cold-open slams → manim BURSTS → face-off → FOMO montage → cliffhanger; content-synced transparent lottie beats; the seek-modulo fix; teaser music synth

Companion skills

hyperframes (composition contract — always invoke when authoring), hyperframes-cli, lottie, video-edit (transcribe + approve webapp), yuv-design-system (brand). This skill orchestrates them.

SKILL.md on GitHub

Sponsors

SnugThe clipboard manager you and your agent can use, safelyAgent CaffeineClose your lid, come back to finished workWaxBlock anything with supervision, without erasing your iPhoneOpen StepsSponsoredFree and open-source skills that make AI coding agents easier to understand, verify, and control.

Details

Repository
github.com/hoodini/ai-agents-skills
Category
AI & Agent Building
First seen
Jun 11, 2026

More from hoodini/ai-agents-skills

All 37
  • Yuv Viral Video
  • Github Trending3.2K
  • Owasp Security3K
  • Mongodb1.7K
  • Mobile Responsiveness1.4K
  • Google Workspace Cli455
  • Web Accessibility455
  • Figma449
  • Mermaid Diagrams407
  • Analytics Metrics380
  • Ux Design Systems270
  • Aws Strands249
  • Bun190
  • Nano Banana Pro189
  • Honest Agent188
  • Fal Ai175
  • Vercel173
  • Langchain169
  • Local Llm Router156
  • Aws Account Management148
  • Aws Agentcore148
  • Copilot Docs147
  • Cloudflare144
  • Copilot Sdk139

Recommended

More AI & Agent Building
1hoodini avataryuv-viral-videohoodini/ai-agents-skillsEdit any selfie or screen-share footage into a viral short-form video in YUV.AI's signature style — Apple-style liquid-glass cards (real CSS backdrop-filter), d0installs
2https-deeplearning-ai avataradding-cli-commandhttps-deeplearning-ai/sc-agent-skills-filesProvides Typer templates, handles registration, and ensures consistency. ALWAYS use this skill when adding or modifying CLI commands. Use when user requests to 0installs
3https-deeplearning-ai avatargenerating-cli-testshttps-deeplearning-ai/sc-agent-skills-filesGenerate pytest tests for Typer CLI commands. Includes fixtures (temp_storage, sample_data), CliRunner patterns, confirmation handling (y/n/--force), and edge c0installs
4https-deeplearning-ai avatarlearning-a-toolhttps-deeplearning-ai/sc-agent-skills-filesCreate learning paths for programming tools, and define what information should be researched to create learning guides. Use when user asks to learn, understand0installs
5https-deeplearning-ai avatarreviewing-cli-commandhttps-deeplearning-ai/sc-agent-skills-filesProvides checklist for reviewing Typer CLI command implementations. Covers structure, Annotated syntax, error handling, exit codes, display module usage, destru0installs
6intertwine avatardspy-advanced-workflowintertwine/dspy-agent-skillsDrive a complete DSPy 3.2.x project end-to-end — spec → program → metric → baseline → GEPA optimize → export → deploy. Orchestrates the other four DSPy skills (0installs