AI Services Market › Use cases › Software & DevOps
Software & DevOpsDone with Dots, Codex, Claude Code Oct 21, 2026
Turn GitHub issues into draft pull requests with long-running agents
The author used Dots for read-heavy long-horizon tasks like reading GitHub issues and making PRs, which were typically poor but gave a starting point.
- What They Did
- The commenter used Dots, a long-running agent tool, alongside their Codex usage for read-heavy, long-horizon tasks. The agent read GitHub issues and drafted pull requests from them. The source gives no further setup detail, and no step where a person intervened is described.
- How It Went
- The pull requests were typically pretty bad, but they gave a starting point. The commenter got much better real-world output from a Claude Code style approach with a human in the loop.
- Worth Knowing
- Running it against regular plan limits was a dealbreaker for the author, who also wanted more control over the context window when the agent drifts off task.
Try it yourself with Dots
Read the open issues in my GitHub repo [repo URL] and pick the [number] most well-defined ones labeled [label or topic]. For each, make a branch and prepare a draft pull request with a first-pass fix, tests if the repo has them, and a short description of what you did and what you're unsure about. Don't merge or push anything beyond draft PRs, and show me the list of PRs you plan to open before you open them.
Do this with a skill.
These AI Services Market playbooks do this exact job.