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AI coding agents can take a task, use tools and make changes across files, but useful results depend on the instructions and repository context you give them—and on your review of the work. These five practices apply across agent workflows. They are not a ranked list of tips extracted from specific “top” GitHub trending repositories: the title’s phrase does not identify a ranking, repositories or date window.
1. Define the goal, boundaries and success criteria
Describe the outcome you want in plain language, then state constraints that could change the implementation: what must remain unchanged, which behavior to preserve, and what counts as done. A request such as “fix the bug” leaves too much open. A stronger one names the visible failure, the expected behavior and any limits on the change.
Cursor’s official documentation summarizes the human role this way: “You set the goal and review the output.” That is a useful model beyond any one product: the agent can do work, but you set its direction and decide whether the result is acceptable.
2. Ground the request in the repository
Give the agent relevant files, existing patterns and the parts of the project it should treat as authoritative. For example, point it to the component that displays the behavior, the test that covers it and a nearby implementation to follow. This reduces the chance that it invents an approach that conflicts with the project’s conventions.
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Cursor recommends grounding prompts in real files and patterns. More broadly, an agent’s output depends partly on the model, its harness—the tools and workflow around it—and the context it receives. A capable agent working from a vague request can still make a poorly fitted change.
3. Ask for a plan before broad changes
For a change spanning several files or affecting a central feature, ask the agent to inspect the relevant code and outline its approach before editing. Review whether the proposed files and steps match the request; correct the plan before work begins if it misses a constraint or expands the scope.
Cursor recommends using Plan mode to review the approach first for larger work. For a small, isolated edit with an obvious check, a separate planning step may add little. Match the amount of up-front review to the size and risk of the change.
4. Require checks, then inspect the changes
Ask the agent to run the project’s relevant checks and report the commands and results. Depending on the project, that might mean a focused test, a broader test suite, a linter or a build. Treat a successful command as evidence about that check—not proof that the change is correct or that every affected behavior is covered.
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Then inspect the diff yourself: confirm that the changes address the request, avoid unrelated edits, fit the project’s patterns and do not introduce obvious security or compatibility problems. GitHub documents code-review and agentic-workflow options, and agent tools can run commands and check results; neither capability removes the need for human review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Match the workflow to the task, and account for cost
Keep straightforward work small enough to verify easily. For broad or consequential work, use a plan, provide focused context and give the result more careful review. That is not just a matter of caution: agent performance can differ by task type.
A 2026 study by Giovanni Pinna, Jingzhi Gong, David Williams and Federica Sarro analyzed 7,156 pull requests. In its dataset, acceptance was 82.1% for documentation tasks and 66.1% for new features; the authors also report that no tested agent led every task category. Those figures describe that study’s sample and method, not a promise about any agent or future project.
Budget for usage as well as review time. GitHub’s documentation says, “Coding agents consume GitHub Actions minutes and AI credits.” The amount depends on the relevant model and token usage, so check the current terms for the service and workflow you use rather than assuming every task has the same cost.
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