Automating Content Generation: A Technical Deep-Dive into Metadata Management

 

TL;DR: I updated the metadata management system for automated content generation, focusing on improving version control and data consistency. This involved modifying several JSON files and adjusting the automation scripts.

The Problem

The initial problem was the lack of standardized metadata management across different content platforms. This led to inconsistencies in versioning and data propagation. Specifically, when generating content for various platforms like Medium, Substack, and Bluesky, the metadata wasn't being updated uniformly, causing discrepancies in published content.

What I Tried First

Initially, I attempted to manually update the metadata JSON files (metadata.json) for each platform. However, this approach quickly became cumbersome and prone to errors, especially when dealing with multiple platforms and content pieces. I realized the need for an automated solution that could handle version bumps and data consistency across all platforms.

The Implementation

The solution involved updating the automation scripts to dynamically generate and update metadata. Key changes included:

Updated metadata.json

{
  "pull_requests": 0,
  "releases": 0,
  "closed_issues": 0,
  "medium_generated": true,
  "substack_generated": true
}

Changes to content-automation/changelog.md

## 2026-07-14 content-automation

### Changed
- Updated Bluesky posts for 2026-07-14 in `content/2026/07/13/content-automation/bluesky_en.json` and `content/2026/07/13/content-automation/bluesky_es.json`

New Bluesky JSON Files

For instance, the new bluesky_en.json file:

[]

Adjustments to Automation Scripts

I modified the scripts to automatically update the version numbers and metadata. This involved using jq to parse and update JSON files:

jq '.medium_generated = true' content/2026/07/14/content-automation/metadata.json

Key Takeaway

The primary lesson from this experience is the importance of automating repetitive tasks, especially those involving data consistency and version control. By implementing a dynamic metadata management system, I significantly reduced the manual effort required and minimized the risk of errors.

What's Next

The next step is to integrate this metadata management system with other parts of the content automation pipeline, ensuring seamless data flow and consistency across all platforms. This will involve further script optimizations and possibly integrating additional tools for more robust data handling.

vibecoding #buildinpublic #contentautomation #metadata #devops

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Part of my Build in Public series — sharing the real process of building SaaS projects from Playa del Carmen, México.

Repo: zaerohell/content-automation · 2026-07-15

#playadev #buildinpublic