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Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name ultimate-ai-content-pipeline description Automated content creation pipeline with AI research, multilingual script generation, and video rendering using Claude/OpenAI and Remotion triggers ["how do I automate content creation with AI research","set up automated video generation pipeline","create multilingual content with Claude and OpenAI","build AI content automation system","generate videos from text with Remotion","automate research and content writing workflow","set up AI marketing content pipeline","create automated social media content system"]
Ultimate AI Content Pipeline
Skill by ara.so — Marketing Skills collection.
This project is a complete automated content creation pipeline that transforms keywords into fully-researched, multilingual articles and videos. It crawls recent data from sources like TechCrunch and Twitter, generates content using Claude/OpenAI, and renders videos using Remotion.
What It Does
Auto-Research : Crawls and analyzes real-time data from news sources and social mediaAI Content Generation : Creates articles in multiple formats (toplist, POV, case study, how-to)Multilingual Support : Generates content in English and Vietnamese simultaneouslyVideo Rendering : Automatically creates infographics and short-form videos from contentMulti-Platform Export : Optimized for Reels, TikTok, Shorts
Installation
git clone https://github.com/pennydinh/marketing-pineline-share.git
cd
Environment Configuration Create a .env.local file in the project root:
ANTHROPIC_API_KEY=your_claude_api_key
OPENAI_API_KEY=your_openai_api_key
RAPIDAPI_KEY=your_rapidapi_key
DATABASE_URL=your_database_url
REMOTION_LICENSE_KEY=your_remotion_license_key
Project Structure ├── src/
│ ├── app/ # Next.js app directory
│ ├── components/ # React components
│ ├── lib/
│ │ ├── ai/ # AI integration (Claude, OpenAI)
│ │ ├── research/ # Web scraping and data collection
│ │ ├── content/ # Content generation logic
│ │ └── video/ # Remotion video rendering
│ └── utils/ # Helper functions
├── remotion/ # Remotion video templates
└── public/ # Static assets
Core Usage Patterns
1. Research & Data Collection import { autoResearch } from '@/lib/research/auto-scan' ;
const researchData = await autoResearch ({
keyword : 'AI automation' ,
sources : ['techcrunch' , 'twitter' , 'linkedin' ],
timeframe : '24h' ,
language : 'en'
});
console .log (researchData.insights );
console .log (researchData.sources );
console .log (researchData.statistics );
2. AI Content Generation with Claude import Anthropic from '@anthropic-ai/sdk' ;
const anthropic = new Anthropic ({
apiKey : process.env .ANTHROPIC_API_KEY ,
});
async function generateContent (
topic : string ,
format : 'toplist' | 'pov' | 'case-study' | 'how-to' ,
language : 'en' | 'vi'
) {
const message = await anthropic.messages .create ({
model : 'claude-3-5-sonnet-20241022' ,
max_tokens : 4000 ,
messages : [
{
role : 'user' ,
content : `Create a ${format} article about ${topic} in ${language} .
Include data-backed insights and current trends.`
}
],
});
return message.content [0 ].text ;
}
const article = await generateContent ('AI Marketing Tools' , 'toplist' , 'en' );
3. Multilingual Content Generation import { generateMultilingualContent } from '@/lib/content/multilingual' ;
const multilingualContent = await generateMultilingualContent ({
topic : 'Content Automation Trends 2026' ,
format : 'how-to' ,
languages : ['en' , 'vi' ],
tone : 'professional' ,
researchData : researchData
});
console .log (multilingualContent.en );
console .log (multilingualContent.vi );
4. Video Generation with Remotion import { bundle } from '@remotion/bundler' ;
import { renderMedia, selectComposition } from '@remotion/renderer' ;
import { webpackOverride } from './remotion/webpack-override' ;
async function renderContentVideo (content : any const bundleLocation = await bundle ({
entryPoint : './remotion/index.ts' ,
webpackOverride,
});
const composition = await selectComposition ({
serveUrl : bundleLocation,
id : 'ContentVideo' ,
inputProps : {
title : content.title ,
points : content.keyPoints ,
duration : 30 ,
},
});
await renderMedia ({
composition,
serveUrl : bundleLocation,
codec : 'h264' ,
outputLocation : `out/${content.slug} .mp4` ,
});
}
5. Complete Content Pipeline import { ContentPipeline } from '@/lib/pipeline' ;
const pipeline = new ContentPipeline ({
aiProvider : 'claude' ,
languages : ['en' , 'vi' ],
outputFormats : ['article' , 'video' , 'infographic' ]
});
const result = await pipeline.run ({
keyword : 'AI Marketing Automation' ,
contentFormat : 'toplist' ,
videoAspectRatio : '9:16' ,
autoPublish : false
});
console .log (result.articles );
console .log (result.videos );
console .log (result.metadata );
API Integration Examples
Research API Integration import axios from 'axios' ;
async function fetchTrendingTopics (const options = {
method : 'GET' ,
url : 'https://trending-topics-api.p.rapidapi.com/topics' ,
params : { category : 'technology' },
headers : {
'X-RapidAPI-Key' : process.env .RAPIDAPI_KEY ,
'X-RapidAPI-Host' : 'trending-topics-api.p.rapidapi.com'
}
};
const response = await axios.request (options);
return response.data ;
}
OpenAI Integration Alternative import OpenAI from 'openai' ;
const openai = new OpenAI ({
apiKey : process.env .OPENAI_API_KEY ,
});
async function generateWithGPT (prompt : string const completion = await openai.chat .completions .create ({
model : 'gpt-4-turbo-preview' ,
messages : [
{
role : 'system' ,
content : 'You are an expert content writer specializing in marketing.'
},
{
role : 'user' ,
content : prompt
}
],
temperature : 0.7 ,
});
return completion.choices [0 ].message .content ;
}
Running the Application
Development Server
Video Rendering
npx remotion render remotion/index.ts ContentVideo out/video.mp4
npx remotion studio
Build for Production
npm run build
npm run start
Common Workflows
Workflow 1: Generate Daily Content import { scheduleContentGeneration } from '@/lib/scheduler' ;
scheduleContentGeneration ({
cron : '0 9 * * *' ,
topics : ['AI trends' , 'Marketing automation' , 'Content strategy' ],
formats : ['toplist' , 'how-to' ],
languages : ['en' , 'vi' ],
autoRender : true ,
autoPublish : false
});
Workflow 2: Trend-Based Content async function generateTrendingContent (const trends = await fetchTrendingTopics ();
const contentPromises = trends.slice (0 , 3 ).map (trend =>generateContent (trend.title , 'pov' , 'en' )
);
const articles = await Promise .all (contentPromises);
for (const article of articles) {
await renderContentVideo (article);
}
return articles;
}
Workflow 3: Custom Content Format interface CustomContentConfig {
topic : string ;
targetAudience : string ;
contentGoal : 'awareness' | 'engagement' | 'conversion' ;
includeDataPoints : boolean ;
}
async function generateCustomContent (config : CustomContentConfig const researchData = await autoResearch ({
keyword : config.topic
});
const prompt = `
Create content about ${config.topic} for ${config.targetAudience} .
Goal: ${config.contentGoal}
${config.includeDataPoints ? 'Include relevant statistics and data points.' : '' }
Research insights: ${JSON .stringify(researchData.insights)}
` ;
return await generateWithGPT (prompt);
}
Configuration Options
Content Generation Config interface ContentConfig {
aiProvider : 'claude' | 'openai' ;
model ?: string ;
temperature ?: number ;
maxTokens ?: number ;
tone ?: 'professional' | 'friendly' | 'humorous' ;
includeImages ?: boolean ;
seoOptimized ?: boolean ;
}
const config : ContentConfig = {
aiProvider : 'claude' ,
model : 'claude-3-5-sonnet-20241022' ,
temperature : 0.7 ,
maxTokens : 4000 ,
tone : 'professional' ,
includeImages : true ,
seoOptimized : true
};
Video Rendering Config interface VideoConfig {
fps : number ;
width : number ;
height : number ;
codec : 'h264' | 'h265' ;
quality : 'low' | 'medium' | 'high' ;
aspectRatio : '16:9' | '9:16' | '1:1' ;
}
const videoConfig : VideoConfig = {
fps : 30 ,
width : 1080 ,
height : 1920 ,
codec : 'h264' ,
quality : 'high' ,
aspectRatio : '9:16'
};
Troubleshooting
API Rate Limits import { RateLimiter } from '@/lib/utils/rate-limiter' ;
const limiter = new RateLimiter ({
maxRequests : 50 ,
windowMs : 60000 ,
});
async function callAIWithRateLimit (prompt : string await limiter.wait ();
return await generateContent (prompt, 'toplist' , 'en' );
}
Error Handling async function safeContentGeneration (topic : string try {
const content = await generateContent (topic, 'toplist' , 'en' );
return { success : true , content };
} catch (error) {
if (error.status === 429 ) {
console .error ('Rate limit exceeded, retry in 60s' );
await new Promise (resolve =>setTimeout (resolve, 60000 ));
return safeContentGeneration (topic);
}
if (error.status === 401 ) {
console .error ('Invalid API key' );
throw new Error ('Check your API keys in .env.local' );
}
console .error ('Content generation failed:' , error);
return { success : false , error : error.message };
}
}
Video Rendering Issues
import { getCompositions } from '@remotion/renderer' ;
async function debugRemotionSetup (try {
const bundleLocation = await bundle ({
entryPoint : './remotion/index.ts' ,
});
const compositions = await getCompositions (bundleLocation);
console .log ('Available compositions:' , compositions);
} catch (error) {
console .error ('Remotion setup error:' , error);
console .log ('Check: 1) remotion/ directory exists, 2) index.ts is valid' );
}
}
Memory Management for Large Content
async function batchContentGeneration (topics : string []const batchSize = 5 ;
const results = [];
for (let i = 0 ; i < topics.length ; i += batchSize) {
const batch = topics.slice (i, i + batchSize);
const batchResults = await Promise .all (
batch.map (topic =>generateContent (topic, 'toplist' , 'en' ))
);
results.push (...batchResults);
if (global .gc ) global .gc ();
}
return results;
}
Best Practices
Always validate research data before passing to AICache research results to avoid redundant API callsUse streaming for long-form content generationImplement retry logic for API failuresMonitor AI costs with usage trackingVersion control your prompt templatesTest video renders before batch processing 