From Raw Commits to Rich Stories: Automating Content Curation

Building a Content Pipeline: How a Bot Learned to Prepare Its Own Stories

The C–projects-bot-social-publisher faced a classic developer dilemma: it could execute tasks brilliantly, but capturing what it had done for public consumption? That required structure, discipline, and a checklist.

The task was straightforward on the surface—create a systematic way to gather and organize development artifacts before transforming them into blog posts. But beneath that simplicity lay a more interesting challenge: how do you codify the process of turning raw work data into narrative gold?

First thing I did was map out what “ingredients” actually meant in this context. We weren’t just talking about git commits or code diffs. A truly useful checklist needed to capture the messier reality of development work: the problems encountered, the architectural decisions made, the trade-offs between approaches, and the moments when something unexpectedly worked (or didn’t). This wasn’t about collecting data—it was about curating context.

The implementation leaned heavily on git integration as the backbone. Each commit message became a narrative thread, but raw commits alone told only part of the story. The real insight came from layering additional metadata: work logs that captured the why behind decisions, documentation snippets that explained the technical landscape, and even transcripts from thinking sessions that revealed the decision-making process itself.

Unexpectedly, the hardest part wasn’t the technical integration. It was standardizing what developers should capture without making the checklist so burdensome that nobody would use it. Too prescriptive, and it becomes busywork. Too loose, and you end up with unusable garbage data. The sweet spot turned out to be category-based organization—grouping artifacts by type (feature_implementation, bug_fix, refactoring, research) rather than forcing a single rigid format.

The pipeline now works like this: as work happens, metadata gets tagged. When it’s time to write, everything flows into a structured format that the content generator can consume. The developer provides raw materials, the system ensures nothing crucial gets lost in translation, and the writer gets everything needed to craft a compelling story.

Here’s something fascinating about this approach: the discipline of preparing content artifacts actually improves the work itself. When developers know they’ll eventually need to explain their decisions to an audience, they make more intentional choices. Comments become clearer. Trade-offs get documented. Debugging sessions become learning opportunities rather than just problem-solving exercises.

What we achieved here was less about perfect data collection and more about building a feedback loop. The same structure that makes content creation easier also makes the development process itself more reflective and intentional. The bot’s work became not just something done, but something documented, understood, and sharable.

The journey continues—each post generated refines what we capture next time, and the cycle of work-to-story becomes smoother with each iteration.

The six stages of debugging: 1. That can’t happen. 2. That doesn’t happen on my machine. 3. That shouldn’t happen. 4. Why does that happen? 5. Oh, I see. 6. How did that ever work? 😄