Show HN: Claude Code workflow: PRDs → GitHub Issues → parallel execution
The problem was that context kept disappearing between tasks. With multiple Claude agents running in parallel, I’d lose track of specs, dependencies, and history. External PM tools didn’t help because syncing them with repos always created friction.
The solution was to treat GitHub Issues as the database. The "system" is ~50 bash scripts and markdown configs that:
- Brainstorm with you to create a markdown PRD, spins up an epic, and decomposes it into tasks and syncs them with GitHub issues - Track progress across parallel streams - Keep everything traceable back to the original spec - Run fast from the CLI (commands finish in seconds)
We’ve been using it internally for a few months and it’s cut our shipping time roughly in half. Repo: https://github.com/automazeio/ccpm
It’s still early and rough around the edges, but has worked well for us. I’d love feedback from others experimenting with GitHub-centric project management or AI-driven workflows.
I talked to and extremely strong engineer yesterday who is basically doing exactly this.
Would love to see a video/graphic of this in action.
How are people using auto-edits and these kind of higher-level abstraction?
There is no magic way. It boils down to less strict inspection.
I try to maintain an overall direction and try to care less about the individual line of code.
Essentially, I'm treating Claude Code as a very fast junior developer who needs to be spoon-fed with the architecture.
Test runner sub agent knows exactly how to run tests, summarize failures etc. It loads up all the context specific to running tests and frees the main agent's context from all that. And so on...
Hopefully, your GitHub tickets are large enough, such as covering one vertical scope, one cross-cutting function, or some reactive work such as bug fixing or troubleshooting.
The reason is that coding agents are good at decomposing work into small tasks/TODO lists. IMO, too many tickets on GitHub will interfere with this.
When we break down an epics into tasks, we get CC to analyze what can be run in parallel and use each issue as a conceptual grouping of smaller tasks, so multiple agents can work on the same issue in parallel.
The issues are relatively large, and depending on the feature, every epic has between 5 to 15 issues. When it's time to work on the issue, your local cloud code will break it down into minute tasks to carry out sequentially.