GitHub Copilot has completely revolutionized how we write software, but that AI magic isn't infinite. For many users, Copilot comes with a standard monthly limit on requests and chat interactions. When you are operating under a strict quota, prompt economy becomes incredibly important. You have to treat your prompts like a finite, heavily rationed utility. Making those prompts go as far as possible is an absolute necessity for maintaining your development velocity until the end of your billing cycle.
Personally, I am a Copilot peasant who simply cannot justify dropping endless cash on a per-token basis. Watching that usage bar inch toward 100% triggers a deeply primal panic in my soul. Once that limit is hit, my productivity plummets through the floorboards. I am abruptly forced to remember how to write boilerplate code with my own two hands like it's 2019. It is a dark, barbaric reality that I actively try to avoid.
The secret to surviving on a peasant's budget is abandoning the single-action prompt. Most developers use Copilot chat like a simple command-line interface by typing something basic like run dotnet test. The agent dutifully runs the test, stops, and waits for your next command. If there are errors, you have to spend another precious prompt asking it to investigate and yet another asking it to fix the issue. You end up burning through your monthly quota on tiny micro-interactions.
A much better approach is to chain your workflows together into comprehensive directives. Why spend four prompts when you can spend one? Try instructing the agent: run dotnet test, identify any errors, fix those errors, run dotnet test again to confirm your changes worked, and commit your work. Front-loading the logic and anticipating the next steps drastically reduces your prompt spend while exponentially increasing the actual work completed per request.
Doling out single-step instructions to an AI is like ordering a sandwich at a deli by requesting the bread, waiting five minutes, asking for the turkey, waiting another five minutes, and then finally requesting the cheese. It is exhausting, incredibly inefficient, and honestly, the AI is probably judging you. Don't spoon-feed the AI directions like you're reading off MapQuest; give it the map and destination and let it drive.
You can stretch your prompts even further by leveraging Copilot Instructions and Agent Skills. Documenting common project workflows completely eliminates the need to explicitly request them in your daily chats. For example, you can set a rigid rule to always run dotnet format after making code changes. Agent skills complement this by directing the agent to autonomously execute complex, multi-step actions that you have pre-defined. This allows you to trigger massive workflows with a minimal initial prompt.
To reach peak prompt frugality, implement Agent Hooks. As outlined in GitHub's documentation, hooks allow you to chain common tasks that automatically execute before or after specific agent actions. You could set a pre-hook to always restore dependencies before a build or a post-hook to clean up temporary directories after a test run. Baking these essential, repetitive tasks directly into the agent's operating procedure completely offloads them from your prompt quota.
How to Implement This in Your Repo
To get started, you can define your project's baseline rules using a custom instructions file. Create a file at .github/copilot-instructions.md and lay out your default expectations:
# .NET Project Copilot Instructions
- Always run `dotnet format` after modifying C# files.
- When generating unit tests, default to xUnit and Moq.
- If asked to "run tests," automatically review the error output on failure, attempt to apply a fix, and re-run the tests to verify.
Next, you can configure Agent Hooks to handle the repetitive setup and teardown tasks. By defining pre and post-execution commands, you save the agent from needing explicit instructions for every minor step. A typical hook configuration might look like this:
# .github/copilot/agent.yml
hooks:
pre_test:
- run: dotnet restore
post_test:
- run: dotnet format
- run: git add .
Chaining your commands, establishing smart instructions, utilizing skills, and leveraging hooks lets you effectively automate the automation. Guard your prompts, string your workflows together, and prove that even a Copilot peasant can code like royalty.
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