remotion
Remotion renderer for json-render that turns JSON timeline specs into videos. Use when working with @json-render/remotion, building video compositions from JSON, creating video catalogs, or rendering AI-generated video timelines.
What this skill does
# @json-render/remotion
Remotion renderer that converts JSON timeline specs into video compositions.
## Quick Start
```typescript
import { Player } from "@remotion/player";
import { Renderer, type TimelineSpec } from "@json-render/remotion";
function VideoPlayer({ spec }: { spec: TimelineSpec }) {
return (
<Player
component={Renderer}
inputProps={{ spec }}
durationInFrames={spec.composition.durationInFrames}
fps={spec.composition.fps}
compositionWidth={spec.composition.width}
compositionHeight={spec.composition.height}
controls
/>
);
}
```
## Using Standard Components
```typescript
import { defineCatalog } from "@json-render/core";
import {
schema,
standardComponentDefinitions,
standardTransitionDefinitions,
standardEffectDefinitions,
} from "@json-render/remotion";
export const videoCatalog = defineCatalog(schema, {
components: standardComponentDefinitions,
transitions: standardTransitionDefinitions,
effects: standardEffectDefinitions,
});
```
## Adding Custom Components
```typescript
import { z } from "zod";
const catalog = defineCatalog(schema, {
components: {
...standardComponentDefinitions,
MyCustomClip: {
props: z.object({ text: z.string() }),
type: "scene",
defaultDuration: 90,
description: "My custom video clip",
},
},
});
// Pass custom component to Renderer
<Player
component={Renderer}
inputProps={{
spec,
components: { MyCustomClip: MyCustomComponent },
}}
/>
```
## Timeline Spec Structure
```json
{
"composition": { "id": "video", "fps": 30, "width": 1920, "height": 1080, "durationInFrames": 300 },
"tracks": [{ "id": "main", "name": "Main", "type": "video", "enabled": true }],
"clips": [
{ "id": "clip-1", "trackId": "main", "component": "TitleCard", "props": { "title": "Hello" }, "from": 0, "durationInFrames": 90 }
],
"audio": { "tracks": [] }
}
```
## Standard Components
| Component | Type | Description |
|-----------|------|-------------|
| `TitleCard` | scene | Full-screen title with subtitle |
| `TypingText` | scene | Terminal-style typing animation |
| `ImageSlide` | image | Full-screen image display |
| `SplitScreen` | scene | Two-panel comparison |
| `QuoteCard` | scene | Quote with attribution |
| `StatCard` | scene | Animated statistic display |
| `TextOverlay` | overlay | Text overlay |
| `LowerThird` | overlay | Name/title overlay |
## Key Exports
| Export | Purpose |
|--------|---------|
| `Renderer` | Render spec to Remotion composition |
| `schema` | Timeline schema |
| `standardComponents` | Pre-built component registry |
| `standardComponentDefinitions` | Catalog definitions |
| `useTransition` | Transition animation hook |
| `ClipWrapper` | Wrap clips with transitions |
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.