n8n Workflow Upgrade (AI YouTube Trend Analysis & Upload System)
Budget: $30 – $250 AUD
We are seeking an experienced n8n Workflow Automation Specialist to seamlessly
combine and enhance existing n8n workflows into one cohesive, AI-powered system. The foundational elements for both YouTube element generation and automated video
uploads are robust and in place, making this a straightforward integration and
optimization task. A key part involves a simple, direct swap of data storage from
Google Sheets to Baserow, and adapting file handling from Google Drive to our existing
MinIO (S3-compatible) setup. This project focuses on automating the generation of YouTube content elements and the subsequent secure upload and scheduling of videos. This system will serve a new
YouTube channel focused on "AI Tools" (reviews, comparisons, tutorials, and business
applications) targeting solopreneurs, content creators, freelancers, and small
businesses.
The goal is to create a robust, end-to-end solution that performs deep YouTube market
research, identifies trending content, generates all necessary text-based creative assets
(titles, descriptions, tags, scripts, thumbnail concepts), and automates the secure
upload and scheduling of final video and thumbnail files. This is a small-sized job, primarily an upgrade and integration of already functioning n8n workflows.
Existing Capabilities (What's Already Working):
We have strong, functional n8n workflows that provide a significant head-start for this
integration:
● For AI Content Element Generation: Our Trending_YouTube_Videos_copy.json
template already successfully handles core YouTube data scraping, providing
valuable metrics like video views, subscriber counts, and URLs (as demonstrated
in the previously provided Excel file). This means the initial data acquisition and
outlier detection are already largely in place.
● For Automated YouTube Upload & Scheduling: Our 3900-automated-youtube- video-scheduling-and-ai-metadata-generation.json template contains established
and working logic for YouTube video uploads and scheduling.
This provides a proven foundation for automating our publishing processes.
These existing components simplify the project significantly, as the specialist will focus
on connecting and refining these proven functionalities rather than building from
scratch.
Key Responsibilities:
1. Workflow Integration & Refinement (AI Content Generation):
○ Refine and integrate core functionalities from provided n8n workflow
templates to create a unified system for data acquisition, AI analysis, and
generation of comprehensive YouTube video elements (titles, descriptions, tags, script outlines, thumbnail prompts, and timestamps).
○ Ensure smooth data flow and efficient processing between all nodes, ultimately populating structured data in Baserow. This includes adapting
existing Google Sheets integration to Baserow.
2. Data Acquisition & Centralization:
○ Integrate additional data sources like Google Trends (via SerpApi) to
enrich trend analysis.
○ Centralize all research data and generated content elements within
Baserow tables, ensuring clear data models.
3. AI Orchestration for Content Elements:
○ Configure and fine-tune existing AI agent nodes (using LLM Routers like
Requesty.ai or OpenRouter) to produce highly optimized YouTube video
elements, leveraging trend data and detailed prompts.
4. Automated YouTube Upload & Scheduling:
○ Adapt existing n8n upload templates to be triggered by a status change
in Baserow (e.g., when a video entry in the Video_Production_Queue table
is marked 'Ready for Upload').
○ Implement logic to retrieve corresponding AI-generated metadata (title, description, tags, timestamps) AND MinIO file names from Baserow.
○ Automate the download of video and thumbnail files from MinIO, followed
by their upload to YouTube, applying all retrieved metadata and scheduling
options.
○ Ensure secure deletion of files from MinIO post-upload.
5. Cost Efficiency & Documentation:
○ Maintain focus on cost-effective API usage and adhere to all API
limitations.
○ Provide clean, well-commented n8n workflow JSONs and comprehensive, "plug & play" setup instructions, making the system easy for the user to
manage.
Project Deliverables:
● Two (2) n8n workflow JSON files: One for content element generation, and one
for automated upload and scheduling. These will be directly importable and
ready for immediate use.
● A Baserow database export file: Pre-configured with all necessary tables for
streamlined data management.
● setup instructions: Clear, step-by-step guidance for all API
credentials and configurations, ensuring the system runs immediately upon
setup.
Provided Resources:
○My existing guidance documents and project briefs.
Main Base n8n Templates (already functioning core components):
○ For AI Content Generation Workflow:
Trending_YouTube_Videos_copy.json (provides YouTube data scraping, views, subscribers, URLs).
○ For Automated YouTube Upload & Scheduling Workflow: 3900- automated-youtube-video-scheduling-and-ai-metadata-generation.json
(provides existing logic for YouTube uploads and scheduling).
Complementary n8n Templates (for specific features/enhancements):
○ Database Youtube Outlier Detector.json
○ AI Youtube Trend Finder Based On Niche.json
○ 4506-generate-youtube-video-metadata-timestamps-tags-description.json
○ 2976-automatically-create-youtube-metadata-with-ai.json
○ 3906-automate-youtube-uploads-with-ai-generated-metadata-from-google- drive.json
○ Samautomation_work___Trending_Topics___AndyNoCode_Skool_Commu
nity.json
○ Excel files (e.g., Trending YouTube Videos Analyser & New Title generator
(TEMPLATE).xlsx - Step 1.csv).
Required Skills & Experience:
● Proven expertise in n8n workflow development and automation.
● Experienced using LLMs (e.g., ChatGPT, Gemini, Claude) to create AI agent roles, commands, examples, and output instructions.
● Strong understanding of API integrations (REST APIs, OAuth), particularly with
YouTube Data API, MinIO (S3-compatible), LLM Routers (Requesty.ai,
OpenRouter), Baserow, SerpApi, Apify, and AI Image Generation APIs (e.g., OpenAI/DALL-E 3, Together.ai, Replicate).
● Proficiency in data manipulation, JSON, and JavaScript within n8n.
● Familiarity with database concepts; experience with Baserow is essential.
● Understanding of YouTube's ecosystem, analytics, and SEO best practices.
● Problem-solving skills, ability to adapt to new requirements, and troubleshoot
complex workflows. Desired Qualifications:
● Experience in building end-to-end content automation systems for video
platforms.
● Knowledge of advanced web scraping techniques and ethical considerations for
large-scale data collection.
Application: Please provide your portfolio of n8n projects, highlighting any relevant
experience with AI, data scraping, YouTube-related automation, or large file handling
with S3-compatible storage. In your application, please also address the following:
● Why do you believe you are suitable for this specific project?
● What is your estimated timeline for completing this project from start to finish?
● What is your earliest availability to begin the project? What's your fixed price offer for this project?
● How many years or months have you been using n8n?
combine and enhance existing n8n workflows into one cohesive, AI-powered system. The foundational elements for both YouTube element generation and automated video
uploads are robust and in place, making this a straightforward integration and
optimization task. A key part involves a simple, direct swap of data storage from
Google Sheets to Baserow, and adapting file handling from Google Drive to our existing
MinIO (S3-compatible) setup. This project focuses on automating the generation of YouTube content elements and the subsequent secure upload and scheduling of videos. This system will serve a new
YouTube channel focused on "AI Tools" (reviews, comparisons, tutorials, and business
applications) targeting solopreneurs, content creators, freelancers, and small
businesses.
The goal is to create a robust, end-to-end solution that performs deep YouTube market
research, identifies trending content, generates all necessary text-based creative assets
(titles, descriptions, tags, scripts, thumbnail concepts), and automates the secure
upload and scheduling of final video and thumbnail files. This is a small-sized job, primarily an upgrade and integration of already functioning n8n workflows.
Existing Capabilities (What's Already Working):
We have strong, functional n8n workflows that provide a significant head-start for this
integration:
● For AI Content Element Generation: Our Trending_YouTube_Videos_copy.json
template already successfully handles core YouTube data scraping, providing
valuable metrics like video views, subscriber counts, and URLs (as demonstrated
in the previously provided Excel file). This means the initial data acquisition and
outlier detection are already largely in place.
● For Automated YouTube Upload & Scheduling: Our 3900-automated-youtube- video-scheduling-and-ai-metadata-generation.json template contains established
and working logic for YouTube video uploads and scheduling.
This provides a proven foundation for automating our publishing processes.
These existing components simplify the project significantly, as the specialist will focus
on connecting and refining these proven functionalities rather than building from
scratch.
Key Responsibilities:
1. Workflow Integration & Refinement (AI Content Generation):
○ Refine and integrate core functionalities from provided n8n workflow
templates to create a unified system for data acquisition, AI analysis, and
generation of comprehensive YouTube video elements (titles, descriptions, tags, script outlines, thumbnail prompts, and timestamps).
○ Ensure smooth data flow and efficient processing between all nodes, ultimately populating structured data in Baserow. This includes adapting
existing Google Sheets integration to Baserow.
2. Data Acquisition & Centralization:
○ Integrate additional data sources like Google Trends (via SerpApi) to
enrich trend analysis.
○ Centralize all research data and generated content elements within
Baserow tables, ensuring clear data models.
3. AI Orchestration for Content Elements:
○ Configure and fine-tune existing AI agent nodes (using LLM Routers like
Requesty.ai or OpenRouter) to produce highly optimized YouTube video
elements, leveraging trend data and detailed prompts.
4. Automated YouTube Upload & Scheduling:
○ Adapt existing n8n upload templates to be triggered by a status change
in Baserow (e.g., when a video entry in the Video_Production_Queue table
is marked 'Ready for Upload').
○ Implement logic to retrieve corresponding AI-generated metadata (title, description, tags, timestamps) AND MinIO file names from Baserow.
○ Automate the download of video and thumbnail files from MinIO, followed
by their upload to YouTube, applying all retrieved metadata and scheduling
options.
○ Ensure secure deletion of files from MinIO post-upload.
5. Cost Efficiency & Documentation:
○ Maintain focus on cost-effective API usage and adhere to all API
limitations.
○ Provide clean, well-commented n8n workflow JSONs and comprehensive, "plug & play" setup instructions, making the system easy for the user to
manage.
Project Deliverables:
● Two (2) n8n workflow JSON files: One for content element generation, and one
for automated upload and scheduling. These will be directly importable and
ready for immediate use.
● A Baserow database export file: Pre-configured with all necessary tables for
streamlined data management.
● setup instructions: Clear, step-by-step guidance for all API
credentials and configurations, ensuring the system runs immediately upon
setup.
Provided Resources:
○My existing guidance documents and project briefs.
Main Base n8n Templates (already functioning core components):
○ For AI Content Generation Workflow:
Trending_YouTube_Videos_copy.json (provides YouTube data scraping, views, subscribers, URLs).
○ For Automated YouTube Upload & Scheduling Workflow: 3900- automated-youtube-video-scheduling-and-ai-metadata-generation.json
(provides existing logic for YouTube uploads and scheduling).
Complementary n8n Templates (for specific features/enhancements):
○ Database Youtube Outlier Detector.json
○ AI Youtube Trend Finder Based On Niche.json
○ 4506-generate-youtube-video-metadata-timestamps-tags-description.json
○ 2976-automatically-create-youtube-metadata-with-ai.json
○ 3906-automate-youtube-uploads-with-ai-generated-metadata-from-google- drive.json
○ Samautomation_work___Trending_Topics___AndyNoCode_Skool_Commu
nity.json
○ Excel files (e.g., Trending YouTube Videos Analyser & New Title generator
(TEMPLATE).xlsx - Step 1.csv).
Required Skills & Experience:
● Proven expertise in n8n workflow development and automation.
● Experienced using LLMs (e.g., ChatGPT, Gemini, Claude) to create AI agent roles, commands, examples, and output instructions.
● Strong understanding of API integrations (REST APIs, OAuth), particularly with
YouTube Data API, MinIO (S3-compatible), LLM Routers (Requesty.ai,
OpenRouter), Baserow, SerpApi, Apify, and AI Image Generation APIs (e.g., OpenAI/DALL-E 3, Together.ai, Replicate).
● Proficiency in data manipulation, JSON, and JavaScript within n8n.
● Familiarity with database concepts; experience with Baserow is essential.
● Understanding of YouTube's ecosystem, analytics, and SEO best practices.
● Problem-solving skills, ability to adapt to new requirements, and troubleshoot
complex workflows. Desired Qualifications:
● Experience in building end-to-end content automation systems for video
platforms.
● Knowledge of advanced web scraping techniques and ethical considerations for
large-scale data collection.
Application: Please provide your portfolio of n8n projects, highlighting any relevant
experience with AI, data scraping, YouTube-related automation, or large file handling
with S3-compatible storage. In your application, please also address the following:
● Why do you believe you are suitable for this specific project?
● What is your estimated timeline for completing this project from start to finish?
● What is your earliest availability to begin the project? What's your fixed price offer for this project?
● How many years or months have you been using n8n?