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0:00 The Real Publishing Bottleneck 0:30 Automating the Full Lifecycle 0:42 Building the YTUpload Skill 0:49 Natural Command Without Metadata 1:03 Autonomous Planning and Decomposition 1:23 Smart Permission Handling 1:36 Real OAuth Authentication Failure 1:56 Redirect URI Debugging and Root Cause 2:15 OAuth Recovery and Token Stabilization 2:31 Cleanup After Debugging 2:38 SEO Title Description and Tags 2:51 Resumable Upload via YouTube API 3:03 Email Notifications and Sheets Tracking 3:10 Second Video Challenge 3:25 Token Reuse Without OAuth Errors 3:45 Stable Multi Video Upload Flow 4:05 Missing Thumbnail Detection 4:20 AI Generated Thumbnail 4:40 Full End to End Orchestration 5:00 Technical Breakdown and Architecture 5:20 Why This Is Beyond Automation 5:40 Autonomous Execution Demo Github Link : https://github.com/OnkarPawar1/youtub... The idea behind this project came from a real problem. As AI creators, many of us generate dozens or even hundreds of videos, but the real bottleneck isn’t production — it’s publishing. For every video, you must write an optimized title under 100 characters, craft a 2000–3000 character SEO description, generate 30+ keywords, design a thumbnail, authenticate via Google OAuth, upload the file, manage privacy settings, update tracking sheets, and send notifications. Individually these tasks are simple, but at scale they create operational friction. So instead of automating one step, I automated the entire publishing pipeline. In this demo, I typed a simple instruction: “Please upload the YouTube video from this directory as a priority.” I did not provide metadata, prepare a title or description, or configure YouTube Studio. The agent interpreted the intent, loaded the custom /yt-upload skill, and generated a structured execution plan. It broke the workflow into logical steps: directory exploration, metadata generation, authentication, and upload. This planning phase was visible in real time, showing reasoning rather than blind execution. During execution, the agent explored the folder, identified video assets, and generated SEO-optimized metadata automatically. The real test came during OAuth authentication, which initially failed due to a redirect URI mismatch with Google’s policies. This was a real configuration issue. Instead of stopping, the system retried the flow, inspected the client_secret settings, maintained the correct localhost redirect, and captured the authorization code after adjusting the flow. Once the token exchange completed and token.json was securely stored, it reverted temporary debugging changes. That cleanup step demonstrated stability, not just recovery. After authentication stabilized, the system generated an 80–100 character SEO title, a 2000+ character description, and 30+ optimized keyword tags. It saved everything into a structured metadata.csv file and executed a batch upload using the YouTube Data API v3 with resumable upload protocol. After upload, it captured the video ID, updated upload_state.json for idempotency tracking, sent an email confirmation, logged details into a Google Sheets tracker via Drive and Sheets APIs, and transitioned the video from unlisted to public after verification — all without opening the YouTube dashboard. This demo demonstrates multi-service orchestration across YouTube Data API v3, Google OAuth 2.0, Google Drive API, Google Sheets API, SMTP email notifications, local filesystem operations, FFmpeg-based duration detection, and AI thumbnail generation. It showcases structured intent interpretation, permission-aware execution, adaptive error handling, state persistence, retry logic, and clean rollback of debugging changes. This is not just automation — it is coordinated workflow execution driven by a defined skill architecture. List of videos uploaded by the Accomplish AI: 1. • What is OpenCode? The Open Source Ecosyste... 2. • Automate Me If You Can - The Accomplish Job 3. • Who Invented Writing? The Incredible Histo... 4. • How Gum was Invented: The Sticky History o... 5. • The Sweet History of Donuts: How They Beca...