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Baixar Zip Baixando... Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name marketing-pipeline-ai-content description AI-powered content automation pipeline that researches, generates scripts, and creates videos automatically using Claude/OpenAI and Remotion triggers ["how do I automate content creation with AI research","set up the marketing pipeline for auto-posting","generate videos from content automatically","use Claude to research and write content","create content pipeline with Remotion rendering","automate social media content generation","build AI content workflow with research crawling","configure the marketing automation pipeline"]
Marketing Pipeline AI Content Automation
Skill by ara.so — Marketing Skills collection.
This skill enables AI coding agents to work with the Ultimate AI Content Pipeline - a TypeScript-based content automation system that handles research, scriptwriting, and video generation using AI (Claude 3, OpenAI) and Remotion for video rendering.
What This Project Does
The Marketing Pipeline is an end-to-end content automation system that:
Auto-crawls research from sources like TechCrunch, a16z, Twitter/X, and LinkedInGenerates content in multiple formats (toplists, POV, case studies, how-tos) using Claude/OpenAICreates multilingual content (English & Vietnamese) with customizable toneRenders videos automatically using Remotion for social media platformsProvides a Next.js interface for managing the entire pipeline
Installation
git clone
cd
Configuration Create a .env.local file in the root directory:
# AI Services
ANTHROPIC_API_KEY=your_claude_api_key
OPENAI_API_KEY=your_openai_api_key
# Research APIs
RAPIDAPI_KEY=your_rapidapi_key
# Content Settings
DEFAULT_LANGUAGE=en
TONE=professional
Project Structure marketing-pipeline/
├── src/
│ ├── app/ # Next.js app directory
│ ├── components/ # React components
│ ├── lib/
│ │ ├── research/ # Research crawling modules
│ │ ├── ai/ # AI generation (Claude/OpenAI)
│ │ ├── render/ # Remotion video rendering
│ │ └── utils/ # Helper functions
│ └── types/ # TypeScript type definitions
├── public/ # Static assets
└── remotion/ # Remotion compositions
Core API Usage
1. Research Content Crawling import { crawlResearch } from '@/lib/research/crawler' ;
interface ResearchOptions {
keyword : string ;
sources : ('techcrunch' | 'a16z' | 'twitter' | 'linkedin' )[];
timeframe : '24h' | '7d' | '30d' ;
}
async function gatherResearch (options : ResearchOptions const research = await crawlResearch ({
keyword : options.keyword ,
sources : options.sources ,
timeframe : options.timeframe ,
});
return research;
}
const data = await gatherResearch ({
keyword : 'AI automation' ,
sources : ['techcrunch' , 'twitter' ],
timeframe : '24h' ,
});
2. AI Content Generation import { generateContent } from '@/lib/ai/generator' ;
import { Anthropic } from '@anthropic-ai/sdk' ;
const anthropic = new Anthropic ({
apiKey : process.env .ANTHROPIC_API_KEY ,
});
interface ContentConfig {
format : 'toplist' | 'pov' | 'case-study' | 'how-to' ;
tone : 'professional' | 'friendly' | 'humorous' ;
language : 'en' | 'vi' ;
research : any ;
}
async function createContent (config : ContentConfig const prompt = `
Based on this research: ${JSON .stringify(config.research)}
Create a ${config.format} article in ${config.language} with a ${config.tone} tone.
Include data-backed insights and real examples.
` ;
const message = await anthropic.messages .create ({
model : 'claude-3-5-sonnet-20241022' ,
max_tokens : 4096 ,
messages : [{
role : 'user' ,
content : prompt,
}],
});
return message.content [0 ].text ;
}
3. OpenAI Alternative import OpenAI from 'openai' ;
const openai = new OpenAI ({
apiKey : process.env .OPENAI_API_KEY ,
});
async function generateWithOpenAI (prompt : string const completion = await openai.chat .completions .create ({
model : 'gpt-4-turbo-preview' ,
messages : [
{
role : 'system' ,
content : 'You are an expert content creator specializing in marketing and social media.' ,
},
{
role : 'user' ,
content : prompt,
},
],
temperature : 0.7 ,
max_tokens : 3000 ,
});
return completion.choices [0 ].message .content ;
}
4. Video Rendering with Remotion import { bundle } from '@remotion/bundler' ;
import { renderMedia, selectComposition } from '@remotion/renderer' ;
import path from 'path' ;
interface VideoConfig {
title : string ;
content : string ;
duration : number ;
format : 'reels' | 'tiktok' | 'shorts' ;
}
async function renderContentVideo (config : VideoConfig const bundleLocation = await bundle ({
entryPoint : path.join (process.cwd (), 'remotion/index.ts' ),
webpackOverride : (config ) => config,
});
const composition = await selectComposition ({
serveUrl : bundleLocation,
id : 'ContentVideo' ,
inputProps : {
title : config.title ,
content : config.content ,
},
});
const dimensions = {
reels : { width : 1080 , height : 1920 },
tiktok : { width : 1080 , height : 1920 },
shorts : { width : 1080 , height : 1920 },
};
await renderMedia ({
composition,
serveUrl : bundleLocation,
codec : 'h264' ,
outputLocation : `out/${config.title} .mp4` ,
...dimensions[config.format ],
});
}
Common Patterns
Complete Content Pipeline import { crawlResearch } from '@/lib/research/crawler' ;
import { generateContent } from '@/lib/ai/generator' ;
import { renderContentVideo } from '@/lib/render/video' ;
async function runContentPipeline (keyword : string try {
console .log ('Starting research...' );
const research = await crawlResearch ({
keyword,
sources : ['techcrunch' , 'twitter' ],
timeframe : '24h' ,
});
console .log ('Generating content...' );
const content = await createContent ({
format : 'toplist' ,
tone : 'professional' ,
language : 'en' ,
research,
});
console .log ('Rendering video...' );
await renderContentVideo ({
title : keyword,
content,
duration : 30 ,
format : 'reels' ,
});
return {
success : true ,
content,
videoPath : `out/${keyword} .mp4` ,
};
} catch (error) {
console .error ('Pipeline error:' , error);
throw error;
}
}
Bilingual Content Generation async function generateBilingualContent (research : any const [englishContent, vietnameseContent] = await Promise .all ([
createContent ({
format : 'pov' ,
tone : 'professional' ,
language : 'en' ,
research,
}),
createContent ({
format : 'pov' ,
tone : 'professional' ,
language : 'vi' ,
research,
}),
]);
return {
en : englishContent,
vi : vietnameseContent,
};
}
Batch Content Creation async function createMultipleFormats (keyword : string const research = await crawlResearch ({
keyword,
sources : ['techcrunch' , 'a16z' ],
timeframe : '7d' ,
});
const formats : Array <'toplist' | 'pov' | 'case-study' | 'how-to' > = [
'toplist' ,
'pov' ,
'case-study' ,
'how-to' ,
];
const contents = await Promise .all (
formats.map ((format ) =>
createContent ({
format,
tone : 'professional' ,
language : 'en' ,
research,
})
)
);
return formats.reduce ((acc, format, index ) => {
acc[format] = contents[index];
return acc;
}, {} as Record <string , string >);
}
Running the Application
Development Server npm run dev
yarn dev
pnpm dev
Visit http://localhost:3000 to access the Next.js interface.
Build for Production
Render Videos Only
API Routes (Next.js)
import { NextRequest , NextResponse } from 'next/server' ;
import { runContentPipeline } from '@/lib/pipeline' ;
export async function POST (request : NextRequest const { keyword, format, language } = await request.json ();
try {
const result = await runContentPipeline (keyword);
return NextResponse .json ({
success : true ,
data : result,
});
} catch (error) {
return NextResponse .json (
{ success : false , error : error.message },
{ status : 500 }
);
}
}
Troubleshooting
API Rate Limits
async function retryWithBackoff<T>(
fn : () => Promise <T>,
maxRetries = 3
): Promise <T> {
for (let i = 0 ; i < maxRetries; i++) {
try {
return await fn ();
} catch (error) {
if (i === maxRetries - 1 ) throw error;
await new Promise (resolve =>setTimeout (resolve, Math .pow (2 , i) * 1000 ));
}
}
throw new Error ('Max retries exceeded' );
}
Video Rendering Memory Issues
const composition = await selectComposition ({
serveUrl : bundleLocation,
id : 'ContentVideo' ,
inputProps : {
title : config.title ,
content : config.content .slice (0 , 500 ),
},
});
Claude/OpenAI Token Limits function truncateContent (text : string , maxTokens = 3000 string {
const maxChars = maxTokens * 4 ;
return text.length > maxChars ? text.slice (0 , maxChars) : text;
}
Environment Variables Reference Variable Required Description ANTHROPIC_API_KEYYes Claude API key from Anthropic OPENAI_API_KEYOptional OpenAI API key (alternative to Claude) RAPIDAPI_KEYYes RapidAPI key for research crawling DEFAULT_LANGUAGENo Default content language (en/vi) TONENo Default content tone
Best Practices
Always validate research data before passing to AI generatorsCache research results to avoid redundant API callsUse environment-specific configs for development vs productionMonitor API usage to stay within rate limitsTest video renders locally before batch processingImplement proper error logging for production debugging 