WRAP up your backlog with GitHub Copilot coding agent

Source: GitHub Blog

Using the WRAP Framework with GitHub Copilot

Engineers at GitHub have been using the GitHub Copilot coding agent for over a year, learning how it can save developers real time and energy. From that experience we distilled a simple, memorable acronym to help you get the most out of the coding agent:

WRAP

Why WRAP Helps

• Backlog overload: Prioritize and tackle a long list of issues by turning each into an atomic task that Copilot can handle.
• Tech‑debt vs. new features: Use the agent to chip away at debt while you focus on shipping.
• Split focus: Let Copilot work on smaller bugs or experiments, freeing you for larger, strategic work.

Quick Example

Issue: “Add pagination to the user list page.”
WRAP applied:

1. Write a concise issue with UI mockup and API contract.
2. Refine the instruction: “Create a UserList component that fetches users with page and limit query parameters, displays 10 per page, and includes Next/Prev buttons.”
3. Atomic task: Implement the pagination logic in a separate usePagination hook.
4. Pair: Run Copilot, review the generated hook, adjust edge‑case handling, and merge.

By following WRAP, you can quickly get up to speed and let the coding agent handle tasks you previously didn’t have time for. Happy coding!

Write Effective Issues

The first step of WRAP is to make sure you write effective issues for the GitHub Copilot coding agent. Think of the issue as onboarding material for a new team member—providing enough context sets the agent up for success.

Guidelines

• Write for a newcomer – Include any background a new developer would need to understand the task. This naturally gives the coding agent the context it needs.
• Craft a descriptive title – The title should state what is being done and where it is being done. Since you can assign many tasks to the agent, clear titles keep your issue panel easy to navigate.
• Add concrete examples – If you already know the desired implementation (e.g., a specific error‑handling pattern), paste a short example in the issue. This greatly increases the chance of getting the exact output you want.

Example

Instead of:

Update the entire repository to use async/await

Write:

Update the authentication middleware to use the newer async/await pattern, as shown in the example below. Add unit tests that verify the changes and cover edge cases.

// Example async/await usage
async function exampleFunction() {
  const result = await promise;
  console.log(result); // "done!"
}

Follow these tips to create clear, actionable issues that help the coding agent deliver high‑quality code quickly.

Refine Your Instructions

The next step of WRAP is to refine the GitHub Copilot custom instructions to improve the results of your coding‑agent‑generated pull requests. There are several types of custom instructions you can create, each suited to different scenarios.

Repository Custom Instructions

Store information that applies to the entire repository.

Example: If you have a Go application and prefer a specific coding style, add those guidelines here. Adding and updating these instructions over time will improve every Copilot interaction within the repository.

• Tip: This is an ideal first use case for the GitHub Copilot coding agent. Ask the agent to generate repository‑level custom instructions for you.
• Guide: Add repository custom instructions

Organization Custom Instructions

Create a set of instructions that apply to all repositories in an organization.

Example: Enforce a universal testing strategy across every project.

• Guide: Add organization custom instructions

Coding‑Agent Custom Agents

Define reusable agents (via a natural‑language text file) that can be scoped to the enterprise, organization, or repository level. These agents automate repetitive development tasks that aren’t needed for every change.

Example: An “Integration Agent” that handles the boilerplate for integrating new products with a specific repository.

• Guide: Create custom agents

Remember: Continuously refine and expand your custom instructions. The more precise and comprehensive they are, the better the Copilot agent will perform when generating code and pull requests.

Atomic Tasks

Coding agents excel at handling small, atomic, and well‑defined tasks. They can also be leveraged for larger problems—just break the problem down into multiple, independent, smaller tasks.

Why break tasks down?

• A single massive request (e.g., “Rewrite 3 million lines of code from Java to Go”) is too broad for an agent to handle efficiently.
• Large changes are hard to test, validate, and review.

Example: Migrating a Java codebase to Go

Instead of one huge request, split the work into focused issues such as:

1. Authentication module

   • Migrate the authentication module to Go.
   • Ensure all existing unit tests pass.

2. Data‑validation utilities

   • Convert the data‑validation utilities package to Go.
   • Preserve the same public API.

3. User‑management controllers

   • Rewrite the user‑management controllers in Go.
   • Keep existing REST endpoints and responses unchanged.

By turning a large problem into these atomic tasks, you gain:

• Easier testing – each piece can be validated in isolation.
• Simpler reviews – pull requests are smaller and more focused.
• Higher reliability – failures are confined to a single component, not the entire system.

Pairing with a Coding Agent

When working with a coding agent, it’s important to remember both its strengths and your own as a human. Understanding this balance leads to smoother collaborations and fewer frustrations when the agent doesn’t behave exactly as expected.

What Humans Do Well

• Understanding the “Why” – Grasp the purpose behind a task and assess whether a proposed change truly solves the underlying problem.
• Navigating Ambiguity – Handle vague or incomplete requirements, filling in details a coding agent would need explicitly.
• Cross‑System Thinking – Anticipate how changes in one repository affect other systems, something an agent may miss.

What a Coding Agent Does Well

• Tireless Execution – Work on multiple tasks simultaneously without fatigue.
• Repetitive Tasks – Update naming conventions across many files or perform other monotonous chores efficiently.
• Exploring Possibilities – Prototype several approaches quickly, providing rapid insights without heavy resource consumption.

Take This with You

Lingering backlog issues no longer stand a chance when you’re equipped with GitHub Copilot and WRAP.

• Need to update a dependency?
• Want more test coverage?
• Looking to adopt new error‑handling patterns across your codebase?
• Want a jump‑start on adding repository instructions using the GitHub Copilot coding agent?

Use WRAP to wrap up your backlog!

Get started with GitHub Copilot →

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Written by

Brittany Ellich – Software Engineer, Platform & Enterprise, GitHub

Jason Etcovitch – Software Engineer, Platform & Enterprise, GitHub

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