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Overview

The Draft Queue is where all AI-generated social content lands after the Social Content Pipeline runs. Every piece of content starts as a draft and moves through a review lifecycle before it gets published. Nothing goes live without manual approval. The queue is managed entirely through API endpoints — giving you full control over the review, approval, scheduling, and publishing workflow.

Draft Lifecycle

Every draft moves through a defined set of statuses:

API Endpoints

List Drafts

Retrieve drafts with optional filters for status, platform, content type, and pagination.
Query Parameters: Response:

Get Single Draft

Retrieve the full details of a single draft including all images and metadata.

Update Draft Status

Move a draft through the lifecycle. Include optional review notes, rejection reason, or scheduled timestamp.
Request Body:

Bulk Approve

Approve multiple drafts at once. Useful after batch review sessions.
Request Body:

Content Stats

Get aggregate statistics about the draft queue and content generation.
Response:

Database Schema

The social_content_drafts table in Supabase stores all generated content:

Storage

Generated images are stored in the social-content Supabase Storage bucket:

Review Workflow

1

Check the Queue

List new drafts filtered by status draft:
2

Review Each Draft

Open individual drafts to review text, images, and hashtags. Check that the Professor High voice is consistent and the content is accurate.
3

Approve, Edit, or Reject

Approve drafts that are ready. Reject those that miss the mark with a reason. Add review notes for context.
4

Schedule Approved Content

Set publish timestamps for approved drafts to build out your content calendar:
5

Publish and Mark as Posted

Copy content to your social media tool or post directly. Then mark the draft as posted:
Use the bulk approve endpoint after batch review sessions to approve multiple drafts at once instead of updating them one by one.
Check the stats endpoint regularly to monitor your content pipeline health — how many drafts are waiting, how many are scheduled, and your generation-to-publish ratio.
Rejected drafts are kept in the database for analytics and pipeline improvement. They are not deleted automatically. Review rejection_reason values periodically to identify patterns that may need prompt tuning.