Let Claude Code Analyze Every GitHub Issue for You
One prompt that points Claude Code at your GitHub issues, has it read the codebase, design a solution, and write the answer straight back onto the issue. Works with Jira and any connected MCP too.
/goal connect to the gh cli or github mcp, pull every issue that has not been analyzed already with a solution design readme file attached, run the analysis against the codebase, design the solution, update the issue. It is important to ask me questions if you are not sure. Ask one question at a time and wait for my response
Here is a Claude Code trick I keep coming back to. Point it at your GitHub issues and let it do the first pass of engineering thinking for you: read the issue, read the code that issue touches, design a solution, and write that solution back onto the ticket.
It sounds like a lot. It's one prompt. Copy the block above, paste it into Claude Code, and watch it work through your backlog one issue at a time.
What the prompt actually does
Read it left to right and it's really a checklist you're handing to an engineer:
- Connect to your issues. It reaches your backlog through the gh CLI or the GitHub MCP, whichever you have wired up.
- Find the untouched ones. It skips any issue that already has a solution-design readme attached, so it only works on tickets nobody has analyzed yet. Run it again next week and it picks up where it left off.
- Analyze against the real codebase. This is the part that matters. It doesn't guess from the title. It opens the files the issue is about and reasons about your actual code.
- Design the solution. It writes up an approach: what to change, where, and the trade-offs.
- Update the issue. The design lands back on the ticket, so the next person to open it, human or agent, starts from a real plan instead of a blank page.
The line that makes it safe
The last sentence is the whole trick: ask me questions if you are not sure. Ask one question at a time and wait for my response.
Without it, an eager agent fills every gap with a confident guess, and you end up reviewing a stack of plausible-but-wrong solution designs. With it, Claude Code stops and asks the second it hits real ambiguity. One question, your answer, then it keeps going. You stay the decision-maker. It does the reading and the typing.
It isn't only GitHub
Swap the source and the same pattern holds. Jira, Linear, a support queue, anything you can reach through a connected MCP. The shape of the work doesn't change: pull the items nobody has looked at, understand them against the code, propose a plan, write it back. Point it at whatever system your team actually lives in.
Make it a routine, not a chore
The real unlock is running this on a schedule instead of by hand. Wire it to fire whenever a new issue is opened, and every ticket gets a first-draft solution design before a human ever reads it. Your morning triage stops being "what is this and where does it live" and becomes "do I agree with the plan." That's a much faster meeting.
This is what using AI properly looks like. Not a chatbot off to the side that you copy-paste into. An agent connected to the tools you already use, doing the boring 80% of the analysis, and pulling you in for the 20% that needs your judgment.
Want help turning prompts like this into standing routines across your engineering org? That's the kind of thing I set up as a fractional CTO. Get in touch →