How to Give an AI IDE Repo Context

The working pattern for repo context for AI IDEs is small enough to review and specific enough to repeat. Give the agent a named task and only the context it needs, then tie the result to a check you can rerun. The table below breaks that into moves.

Each of those moves fails in its own particular way, and the order they come in is doing more work than it looks like it is. Skip the boundary and you get a diff nobody wants to read, because the agent has quietly rewritten files you never meant to open. Thin context fails more quietly: the code compiles, the tests pass, and it ignores every pattern the rest of the repo follows. Then there's the check, which to me is the real one, because it's the whole difference between a result you verified and a result you're taking someone's word for.

The step people skip is the plan. I think it's because it feels like overhead, thirty seconds of nothing visibly happening while you're trying to get work done. Actually, that's not quite the reason. It's that the cost of skipping it lands much later, so it never registers as the mistake it was. If the plan names files you didn't expect, you've learned something for free. If it names the right ones, you've got a reference to check the diff against when it arrives. And once there are four hundred lines on the screen, changing the approach means throwing that work away, which nobody is good at.

Yes. The frame below is a starting point, not a script. Fill in your own files, constraints and done state, and keep the plan step so the agent commits to an approach before it writes code.

Task: [one outcome]
Context: [files, errors, docs and examples]
Boundary: [what not to touch]
Done when: [test, typecheck, screenshot or review proof]

Before editing, write a short plan with files, risk and checks.

The Boundary line is the one people leave out, and probably the one doing the most work. Without it every file the agent can reach is fair game, so you end up reviewing incidental edits to config and imports and some helper you'd forgotten existed, all mixed in with the change you actually wanted. Naming what not to touch takes a few seconds. Reverting it afterwards does not.

"Done when" has to name something you can run. A test name, a typecheck, a command whose output you can read, a screenshot of one specific state. "Done when the bug is fixed" doesn't count, and I'd say that's the single most common version of this mistake, because it quietly hands the judgment back to the agent, and the agent is going to tell you it's finished either way.

Running repo context for AI IDEs as a team comes down to one habit: leave a trail the next reviewer can follow. The steps below keep the prompt and its proof attached to the change, so nobody has to reverse-engineer what the agent did.

1. 1Pick one real backlog item with a clear owner and expected result.
2. 2Add only the context the agent needs: files, failing output, constraints and done state.
3. 3Ask for a plan before code when the task touches more than one file.
4. 4Run checks that match the risk: unit test, typecheck, visual pass or review checklist.
5. 5Capture the prompt, diff, result and reviewer note so the workflow can be repeated.

Keep whatever lets you rerun the work or hand it to someone else. A finished task is the merged code plus the short trail that explains how it got there.

• The prompt or plan that shaped the work.
• The files changed and the reason each file changed.
• The command, screenshot or review note that proved the result.
• The rule, checklist or template you would reuse next time.

Put every repo the feature touches into one workspace. Cursor's workspace defines the domain the agent can see, so a backend repo, a frontend repo, and the docs sitting together let it trace a feature end to end instead of guessing at the boundaries.

The setup is mechanical. Make one empty parent folder, git clone each repo into it, then open the parent folder as the workspace.

Ramp into an unfamiliar codebase with a recon prompt

Reading context is also how you onboard fast. Point the agent at a repo you've never seen and ask for a structured digest instead of clicking through files for an afternoon. One security engineer framed it as models being good at ramping you in, with no dumb questions, so you can move the needle on a system you didn't know last week.