I had planned to write this next week, but the version I made disappeared, and redoing work that's already done isn't any fun. It's like reheating french fries: technically edible but not as good. So here we go with something fresh and direct.
Quick summary: don't throw Copilot into the repository without clear instructions. Write instructions for the repository, test them, adjust them, and repeat. Whether you use Microsoft's tool, Coding Agent, or a custom chat mode like Instructionalist, the goal is the same: keep Copilot working within your project's rules.
Context: this text is a continuation of the most-read article on GitHub Copilot instructions and gathers new lessons learned from using Copilot in enterprise codebases. Much of the previous content still applies, but there are new techniques for starting and maintaining instructions that actually work.
Why repository instructions matter: imagine a legacy application that no one wants to touch, and a new senior developer arrives. You wouldn't send them to refactor without context and explanation. Exactly the same thing happens when you enable Copilot without guidelines. Copilot will generate something confidently based on the worst patterns in your repo if you don't give it direction.
Planning 15 minutes at the start is worth gold. Spend a quarter of an hour explaining why certain parts are configured a certain way, which files are dangerous to touch, and what critical dependencies exist. That's exactly what good Copilot instructions do: they act as a quick onboarding for any agent that will work on the code.
Remember, this isn't a set-it-and-forget-it thing. Instructions must evolve with the application. Changes in the app mean changes in the instructions.
What are repo instructions: they are the opportunity to give Copilot a tour before it starts suggesting changes. They work wherever Copilot runs: IDE, GitHub, and mobile, according to the documentation. Let's place them on the main branch so the whole team and tools use the same rules, styles, and the list of files that shouldn't be touched.
Practical tip: don't use the file to document style rules already covered by the formatter or linter. Instead, use the instructions to explain architectural decisions, anti-patterns, testing goals, and deployment notes. If you have specific instructions for the database or a certain domain, separate them into files inside .github/instructions and use front matter applyTo to associate them with specific code paths.
Three good ways to get started:
First option: Microsoft in VS Code. At first it was very basic, but it has improved. Now it reads the repository, detects structure, workflows, and automations. It's practical and generates a usable draft that's worth trimming and refining before committing it.
How to use it in VS Code: press the chat menu or the command Chat Generate Workspace Instructions File and let it analyze the workspace. Then edit the result and remove unnecessary filler.
Second option: Coding Agent. This option runs on models with a good sense of architecture and delivers a first draft focused on principles and strategy. It's safe for PRs because it always creates its own branch. The key with Coding Agent is to be specific to avoid it guessing and generating unwanted documents. It usually includes useful sections like content and style guides, but sometimes it tries to add full contributions that might belong in a separate CONTRIBUTING.md.
Third option: Instructionalist, a custom chat mode. This approach requires active participation from the repository expert and works like a Q&A session to capture what Copilot can't deduce: service level agreements, fragile dependencies, incident restart instructions, and operational lore. It's the one that best defines the project's intent and stance by explaining purpose, value, anti-patterns, and testing goals. It requires more time upfront but produces instructions more aligned with what's actually needed.
Comparative results: Microsoft tends to describe the project map: structure and concepts. Coding Agent focuses on the rules of the game and principles. Instructionalist describes the agent's role and the desired intent for contributions. The best practice is to combine the best of each approach: structure, clear rules, and purpose context.
Practical recommendations: keep the main file lightweight with the essentials and save additional ideas in a future review folder. Update the instructions whenever something changes. Outdated instructions are as dangerous as having none.
How this helps Q2BSTUDIO: at Q2BSTUDIO, we are specialists in software development and custom applications, and we understand that effectively integrating artificial intelligence solutions into the workflow is critical. By preparing clear instructions for agents like Copilot, we help teams reduce risks, maintain quality standards, and speed up deliveries. We also integrate cybersecurity practices so that automatic suggestions respect security standards and don't expose secrets or attack vectors.
Services connected to these practices: custom applications and custom software with artificial intelligence integration, implementation of cloud services AWS and Azure, business intelligence and Power BI services for visualization and decision-making, implementation of AI agents and AI solutions for companies, and cybersecurity consulting. Q2BSTUDIO supports from repository auditing to production deployment of pipelines and models, ensuring that instructions and automations are secure and sustainable.
Example of content worth including in repository instructions for companies: project purpose, maintenance level, critical dependencies, expected patterns and anti-patterns, deployment rules, required tests, emergency contacts, security policies, and references to lint and formatters. Adding notes about AI agents and trust limits helps control automatic code generation in sensitive environments.
Conclusion: there is no single perfect solution. The ideal is a hybrid with structure, rules, and purpose context. Invest a little time in creating and testing instructions, and you'll see enormous returns in quality and speed. If you need help, Q2BSTUDIO offers services to design these instructions, train teams in their use, and support the integration of artificial intelligence into your projects with an emphasis on cybersecurity and AWS and Azure cloud compliance.
Invitation: try generating instructions with Microsoft or Coding Agent, then compare with a custom mode like Instructionalist. Trim what's extra, add what's missing, and keep it updated. Share success stories or lessons learned to keep improving the global practice.
Final note: this article was created with support from AI tools, but only after giving them the necessary guidance, just as we do at Q2BSTUDIO to ensure that artificial intelligence serves real business goals without compromising security or quality.
