ChatGPT, JSON, and GitHub for Internal Management

One of the most useful patterns I’ve discovered is how simple it is to bridge ChatGPT with GitHub issues using JSON.

At its core, the workflow looks like this: I let ChatGPT generate structured issue data, copy it out as JSON, and then feed it directly into my Issues API. That means a rough idea in natural language can become a formatted issue with labels, body text, and repo assignment in one move.

The System I Built

The routes are already set up for scale. With them, I can:

• POST arrays of issues at once, turning a brainstorm into dozens of tickets.
• Update issues in place if scope or details change.
• Close them out automatically without a round of manual cleanup.

On the frontend I built helpers:

• A JSON issue creator where I paste structured output.
• Drawers that surface live repo issues inline.
• A sidebar goal tracker tied to repositories.
• Stars and favorites to give lightweight prioritization—“must watch” repos bubble up, while the long tail stays out of the way.

The ingredients are there: generation, ingestion, visualization, prioritization.

Where It Breaks Down

But here’s the truth: I don’t use it enough.

The workflow exists, it works, but habits lag behind tooling. Too often I fall back to scratchpads or ad-hoc notes instead of pushing everything through the JSON → GitHub loop. It’s not because the system fails—it’s because my behavior defaults to fast and familiar.

That mismatch creates inconsistency. Instead of one source of truth, I end up with scattered inputs. The system is more consistent than I am.

Why It Matters

Every time I force myself to use it, the payoff is obvious:

• Traceability: I can follow a thought from initial prompt to GitHub history.
• Clarity of goals: issues cluster around repositories and initiatives.
• Progress visibility: closed issues show momentum, not just activity.
• Ownership: this is a management layer I control, not one rented from an external SaaS.

And because it’s mine, I can bend it however I need:

• Merge AI-generated drafts into larger projects.
• Reorganize issues when goals shift.
• Refactor the UI without losing history.

What’s Next

So this exposé isn’t a victory lap—it’s a reminder. The bridge between AI, JSON, and GitHub is working. The next step isn’t technical, it’s behavioral:

• Daily discipline. Use the system every day, not occasionally.
• Friction removal. Cut steps until it feels easier than scratchpads.
• Consistency through simplicity. The less I think about the process, the more likely I’ll use it.

That’s how experiments become operating systems: not by being clever, but by becoming unavoidable in daily practice.

Open Questions

• How do I make JSON-first issue creation feel natural, not forced?
• Where should AI live in the loop—drafting, triaging, or even closing issues automatically?
• Can this grow from a personal tool into a team-level operating system?

Those are questions for future labs. For now, the system is in place. The challenge is me.