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Baixar Zip Baixando... Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name marketing-pipeline-content-automation description AI-powered content pipeline for automated research, scriptwriting, video generation and multi-format content creation triggers ["automate content creation with AI research and video generation","set up automated marketing content pipeline","generate videos from text using Remotion","create multilingual content with Claude and OpenAI","scrape trending news for content research","build automated social media content workflow","generate infographics and short-form videos automatically","schedule automated content publishing"]
Marketing Pipeline Content Automation
Skill by ara.so — Marketing Skills collection.
This skill enables AI agents to use the Ultimate AI Content Pipeline - a comprehensive TypeScript-based system that automates content creation from research to video generation. The pipeline crawls trending news, generates multi-format content in multiple languages, and renders videos/infographics automatically using Remotion.
What This Project Does
The Marketing Pipeline automates the entire content creation workflow:
Auto-Research : Crawls news from TechCrunch, a16z, Twitter/X, LinkedIn for trending topicsAI Content Generation : Creates articles in multiple formats (listicles, POV, case studies, how-tos) using Claude/OpenAIMulti-language Support : Generates content in English and Vietnamese simultaneouslyVideo Rendering : Converts content to short-form videos and infographics via RemotionPlatform Optimization : Exports for Reels, TikTok, Shorts with proper aspect ratios
Installation
git clone https://github.com/pennydinh/marketing-pineline-share.git
cd marketing-pineline-share
npm install
yarn install
pnpm install
Environment Configuration Create a .env.local file in the project root:
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_claude_key
RAPIDAPI_KEY=your_rapidapi_key
REMOTION_LICENSE_KEY=your_remotion_key
DATABASE_URL=your_database_connection
FACEBOOK_PAGE_TOKEN=your_fb_token
LINKEDIN_ACCESS_TOKEN=your_linkedin_token
Development Server
Project Structure marketing-pineline-share/
├── src/
│ ├── app/ # Next.js app directory
│ ├── components/ # React components
│ ├── lib/
│ │ ├── ai/ # AI integration (Claude, OpenAI)
│ │ ├── crawler/ # News crawling logic
│ │ ├── video/ # Remotion video generation
│ │ └── utils/ # Helper functions
│ └── types/ # TypeScript types
├── public/ # Static assets
└── remotion/ # Remotion video templates
Key APIs and Usage Patterns
1. News Research & Crawling
import axios from 'axios' ;
interface NewsArticle {
title : string ;
url : string ;
publishedAt : string ;
source : string ;
summary : string ;
}
export async function scrapeNewsForTopic (
topic : string ,
timeRange : '24h' | '7d' = '24h'
): Promise <NewsArticle []> {
const sources = ['techcrunch' , 'a16z' , 'twitter' , 'linkedin' ];
const articles : NewsArticle [] = [];
for (const source of sources) {
const response = await axios.get (
`https://api.rapidapi.com/v1/news/${source} ` ,
{
headers : {
'X-RapidAPI-Key' : process.env .RAPIDAPI_KEY ,
'X-RapidAPI-Host' : 'news-api.rapidapi.com' ,
},
params : {
q : topic,
timeRange,
language : 'en' ,
},
}
);
articles.push (...response.data .articles );
}
return articles;
}
2. AI Content Generation with Claude
import Anthropic from '@anthropic-ai/sdk' ;
const anthropic = new Anthropic ({
apiKey : process.env .ANTHROPIC_API_KEY ,
});
interface ContentRequest {
topic : string ;
format : 'toplist' | 'pov' | 'case-study' | 'how-to' ;
language : 'en' | 'vi' ;
tone : 'professional' | 'friendly' | 'humorous' ;
researchData : any [];
}
export async function generateContent (
request : ContentRequest
): Promise <string > {
const systemPrompt = buildSystemPrompt (request);
const message = await anthropic.messages .create ({
model : 'claude-3-5-sonnet-20241022' ,
max_tokens : 4096 ,
system : systemPrompt,
messages : [
{
role : 'user' ,
content : `Generate a ${request.format} article about "${request.topic} "
in ${request.language} with a ${request.tone} tone.
Use this research data: ${JSON .stringify(request.researchData)} ` ,
},
],
});
return message.content [0 ].type === 'text'
? message.content [0 ].text
: '' ;
}
function buildSystemPrompt (request : ContentRequest string {
const formatInstructions = {
'toplist' : 'Create a numbered list article with clear benefits and examples' ,
'pov' : 'Write from a personal perspective with strong opinions' ,
'case-study' : 'Analyze a real example with data and insights' ,
'how-to' : 'Provide step-by-step instructions with actionable tips' ,
};
return `You are an expert content creator specializing in ${request.format} articles.
${formatInstructions[request.format]}
Always include recent data, statistics, and credible sources.
Write in ${request.language} with a ${request.tone} tone.` ;
}
3. OpenAI Integration (Alternative)
import OpenAI from 'openai' ;
const openai = new OpenAI ({
apiKey : process.env .OPENAI_API_KEY ,
});
export async function generateContentOpenAI (
topic : string ,
researchData : any []
): Promise <string > {
const completion = await openai.chat .completions .create ({
model : 'gpt-4-turbo-preview' ,
messages : [
{
role : 'system' ,
content : 'You are a marketing content expert who creates engaging, data-driven articles.' ,
},
{
role : 'user' ,
content : `Create an article about "${topic} " using this research:
${JSON .stringify(researchData)} ` ,
},
],
temperature : 0.7 ,
max_tokens : 3000 ,
});
return completion.choices [0 ].message .content || '' ;
}
4. Video Generation with Remotion
import { bundle } from '@remotion/bundler' ;
import { renderMedia, selectComposition } from '@remotion/renderer' ;
import path from 'path' ;
interface VideoConfig {
content : string ;
title : string ;
platform : 'reels' | 'tiktok' | 'shorts' ;
duration : number ;
}
export async function generateVideo (config : VideoConfig Promise <string > {
const compositionId = 'ContentVideo' ;
const bundleLocation = await bundle (
path.join (process.cwd (), 'remotion/index.ts' )
);
const composition = await selectComposition ({
serveUrl : bundleLocation,
id : compositionId,
inputProps : {
title : config.title ,
content : config.content ,
platform : config.platform ,
},
});
const dimensions = getPlatformDimensions (config.platform );
const outputLocation = path.join (
process.cwd (),
'public' ,
'videos' ,
`${Date .now()} .mp4`
);
await renderMedia ({
composition : {
...composition,
width : dimensions.width ,
height : dimensions.height ,
durationInFrames : config.duration * 30 ,
},
serveUrl : bundleLocation,
codec : 'h264' ,
outputLocation,
inputProps : composition.defaultProps ,
});
return outputLocation;
}
function getPlatformDimensions (platform : string const dimensions = {
reels : { width : 1080 , height : 1920 },
tiktok : { width : 1080 , height : 1920 },
shorts : { width : 1080 , height : 1920 },
default : { width : 1920 , height : 1080 },
};
return dimensions[platform] || dimensions.default ;
}
5. Complete Pipeline Orchestration
import { scrapeNewsForTopic } from '../crawler/news-scraper' ;
import { generateContent } from '../ai/content-generator' ;
import { generateVideo } from '../video/render-video' ;
interface PipelineConfig {
topic : string ;
format : 'toplist' | 'pov' | 'case-study' | 'how-to' ;
languages : ('en' | 'vi' )[];
generateVideo : boolean ;
platform ?: 'reels' | 'tiktok' | 'shorts' ;
}
export async function runContentPipeline (config : PipelineConfig try {
console .log ('🔍 Starting research...' );
const researchData = await scrapeNewsForTopic (config.topic , '24h' );
console .log ('✍️ Generating content...' );
const contents = {};
for (const lang of config.languages ) {
const content = await generateContent ({
topic : config.topic ,
format : config.format ,
language : lang,
tone : 'professional' ,
researchData,
});
contents[lang] = content;
}
let videoPath = null ;
if (config.generateVideo && config.platform ) {
console .log ('🎬 Rendering video...' );
videoPath = await generateVideo ({
content : contents['en' ],
title : config.topic ,
platform : config.platform ,
duration : 30 ,
});
}
console .log ('✅ Pipeline complete!' );
return {
research : researchData,
contents,
videoPath,
};
} catch (error) {
console .error ('❌ Pipeline failed:' , error);
throw error;
}
}
6. Next.js API Route Example
import { NextRequest , NextResponse } from 'next/server' ;
import { runContentPipeline } from '@/lib/pipeline/orchestrator' ;
export async function POST (request : NextRequest try {
const body = await request.json ();
const { topic, format, languages, generateVideo, platform } = body;
if (!topic || !format) {
return NextResponse .json (
{ error : 'Topic and format are required' },
{ status : 400 }
);
}
const result = await runContentPipeline ({
topic,
format,
languages : languages || ['en' , 'vi' ],
generateVideo : generateVideo || false ,
platform,
});
return NextResponse .json (result);
} catch (error) {
console .error ('API Error:' , error);
return NextResponse .json (
{ error : 'Content generation failed' },
{ status : 500 }
);
}
}
Frontend Integration
'use client' ;
import { useState } from 'react' ;
export default function ContentGenerator (const [loading, setLoading] = useState (false );
const [result, setResult] = useState (null );
async function handleGenerate (e : React .FormEvent <HTMLFormElement >preventDefault ();
setLoading (true );
const formData = new FormData (e.currentTarget );
const payload = {
topic : formData.get ('topic' ),
format : formData.get ('format' ),
languages : ['en' , 'vi' ],
generateVideo : formData.get ('generateVideo' ) === 'on' ,
platform : formData.get ('platform' ),
};
try {
const response = await fetch ('/api/generate' , {
method : 'POST' ,
headers : { 'Content-Type' : 'application/json' },
body : JSON .stringify (payload),
});
const data = await response.json ();
setResult (data);
} catch (error) {
console .error ('Generation failed:' , error);
} finally {
setLoading (false );
}
}
return (
<form onSubmit ={handleGenerate} className ="space-y-4" >
<input
name ="topic"
type ="text"
placeholder ="Enter topic (e.g., AI Marketing Tools)"
required
className ="w-full p-2 border rounded"
/>
<select name ="format" required className ="w-full p-2 border rounded" >
<option value ="toplist" > Top List</option >
<option value ="pov" > Point of View</option >
<option value ="case-study" > Case Study</option >
<option value ="how-to" > How-to Guide</option >
</select >
<label className ="flex items-center gap-2" >
<input type ="checkbox" name =
Generate Video
Configuration Files
TypeScript Configuration Ensure tsconfig.json includes:
{
"compilerOptions" : {
"target" : "ES2020" ,
"lib" : [ "dom" , "dom.iterable" , "esnext" ] ,
"allowJs" : true ,
"skipLibCheck" : true ,
"strict" : true ,
"forceConsistentCasingInFileNames" : true ,
"noEmit" : true ,
"esModuleInterop" : true ,
"module" : "esnext" ,
"moduleResolution" : "bundler" ,
"resolveJsonModule" : true ,
"isolatedModules" : true ,
"jsx" : "preserve" ,
"incremental" : true ,
"paths" : {
"@/*" : [ "./src/*" ]
}
}
}
Remotion Configuration
import { Config } from '@remotion/cli/config' ;
Config .setVideoImageFormat ('jpeg' );
Config .setOverwriteOutput (true );
Config .setConcurrency (4 );
Config .setCodec ('h264' );
Common Patterns
Pattern 1: Batch Content Generation async function generateBatchContent (topics : string []const results = await Promise .all (
topics.map (topic =>runContentPipeline ({
topic,
format : 'toplist' ,
languages : ['en' , 'vi' ],
generateVideo : false ,
})
)
);
return results;
}
Pattern 2: Scheduled Content Creation
import cron from 'node-cron' ;
cron.schedule ('0 9 * * *' , async () => {
const trendingTopics = await fetchTrendingTopics ();
for (const topic of trendingTopics.slice (0 , 3 )) {
await runContentPipeline ({
topic : topic.name ,
format : 'toplist' ,
languages : ['en' , 'vi' ],
generateVideo : true ,
platform : 'reels' ,
});
}
});
Pattern 3: Content Variation Testing async function generateVariations (topic : string const formats = ['toplist' , 'pov' , 'case-study' , 'how-to' ] as const ;
const variations = {};
for (const format of formats) {
variations[format] = await generateContent ({
topic,
format,
language : 'en' ,
tone : 'professional' ,
researchData : [],
});
}
return variations;
}
Troubleshooting
API Rate Limits
import pLimit from 'p-limit' ;
const limit = pLimit (3 );
const results = await Promise .all (
items.map (item =>limit (() => apiCall (item)))
);
Video Rendering Memory Issues
Config .setConcurrency (2 );
Config .setChromiumDisableWebSecurity (true );
import { cleanupArtifacts } from '@remotion/renderer' ;
await cleanupArtifacts ();
Missing Environment Variables function validateEnv (const required = [
'OPENAI_API_KEY' ,
'ANTHROPIC_API_KEY' ,
'RAPIDAPI_KEY' ,
];
const missing = required.filter (key =>env [key]);
if (missing.length > 0 ) {
throw new Error (
`Missing required environment variables: ${missing.join(', ' )} `
);
}
}
validateEnv ();
Error Handling Best Practices async function safeGenerateContent (config : ContentRequest const maxRetries = 3 ;
let attempt = 0 ;
while (attempt < maxRetries) {
try {
return await generateContent (config);
} catch (error) {
attempt++;
if (attempt >= maxRetries) throw error;
await new Promise (resolve =>setTimeout (resolve, Math .pow (2 , attempt) * 1000 )
);
}
}
}
Build and Deployment
npm run build
npm run start
npx remotion bundle remotion/index.ts public/bundle
npx remotion render public/bundle ContentVideo output.mp4
This skill provides comprehensive coverage of the marketing content automation pipeline, enabling AI agents to help developers implement automated content creation workflows with research, AI generation, and video rendering capabilities.
"generateVideo"
</label >
<select name ="platform" className ="w-full p-2 border rounded" >
<option value ="reels" >
</option >
<option value ="tiktok" >
</option >
<option value ="shorts" >
</option >
</select >
<button
type ="submit"
disabled ={loading}
className ="w-full bg-blue-600 text-white p-2 rounded disabled:bg-gray-400"
>
</button >
<div className ="mt-4 p-4 bg-gray-100 rounded" >
<h3 className ="font-bold" >
</h3 >
<pre className ="mt-2 text-sm overflow-auto" >
</pre >
</div >
</form >
