How the GitHub legal team used Copilot CLI to streamline their workflows

GitHub's legal team, including lawyers and program managers without engineering backgrounds, used GitHub Copilot CLI to build custom tools that automate repetitive legal work. Principal Product Counsel Ngandu Kasuku created terms-ai, a contract drafting tool that cut his review and drafting time roughly in half, while Online Safety Counsel Jesse Geraci built workflows to analyze source code for DMCA notices. The team's success demonstrates that non-technical professionals can leverage AI to create their own productivity tools.

Get started with GitHub Copilot CLI https://github.com/features/copilot/cli How the GitHub legal team used Copilot CLI to streamline their workflows Learn how to build tools to simplify how you work—without writing a single line of code. Whether you are just starting out or are not in a technical role at all, you likely already have the skills you need to build your own tools. If you have ever thought “I’m not technical enough to build that,” this post is for you. Let me introduce the team. We are lawyers, program managers, and business professionals—not engineers. A large part of our work is often repetitive, like reviewing the same kinds of contracts over and over or answering the same legal questions, and our prior guidance is frequently recycled. These are problems AI could help solve, but we lacked confidence in how to build the right tools. That is where GitHub Copilot CLI https://github.com/features/copilot/cli came in. We asked for what we wanted in plain language, plugged into our repos, and saw real changes fast. “I could never code” turned into “I just built something,” and that habit spread on its own until every one of us was building something. What follows are two real accounts of people who did exactly that. Plus, watch the videos for two additional stories. Why I built an internal drafting style guide The following is a first-person account from Ngandu Kasuku, Principal Product Counsel. I’m a product attorney, but commercial work remains a sizable part of my practice. Around March or April, I found myself buried in partnership deals involving data, infrastructure, and product integrations. No two deals looked quite alike, so each new matter felt like starting over. I started using Copilot CLI to manage the surge, which helped, but also had some problems. Then, after seeing what others had built with Copilot, I realized I wasn’t thinking big enough. Instead of using AI for one task at a time, I could build something around the way I work. So, I created a contract drafting tool using Copilot CLI. I called it terms-ai, which I admit isn’t the most original name. I started by scaffolding the project and storing key documents in a repository. This gave me one place to organize and version the instructions, drafting resources, and workflows that guide the AI. That structure made the results more consistent and reduced the copying and pasting that had slowed me down when I was using a library of prompts. One of the tool’s main features is an internal drafting style guide. Since my days as a commercial lawyer, I’ve favored plain language. I never understood why contracts needed words like “heretofore” and “therewith.” When I discovered that an entire legal drafting movement shared this view, I used its principles as the foundation for my style guide. I also built a library of agreements I had already completed. Now, when an existing partner sends over an addendum or a new agreement, the tool can draw on that earlier work. These agreements remain in an approved, access-controlled internal environment. The tool and its general workflow are open source. The agreements and other sensitive information aren’t part of the open source repository. Since I began using terms-ai, I’ve cut my review and drafting time roughly in half. My provisions are more consistent across agreements, and the drafts reflect the plain style I prefer. The tool still has a long way to go. But the biggest lesson wasn’t that AI could help me draft faster. It was that I could use AI to build a tool around my own judgment, experience, and way of working. How I built legal workflows without writing traditional code The following is a first-person account from Jesse Geraci, Online Safety Counsel. I started with a narrow problem. We needed to analyze source code quickly and accurately to evaluate DMCA Digital Millennium Copyright Act notices. The original project began as a set of GitHub Copilot instructions for recurring tasks like DMCA triage, comparing code, license checks, and circumvention review. We wanted to turn the messy, one-off prompt work that everyone was doing independently into something repeatable that a legal team could trust to gather the right facts and analyze the data consistently. I was surprised at how far I could go without engineering support. The core “programming” was plain-language files consisting of workflow instruction sets, policy reference materials, and templates for writing reports. Instead of writing source code, I was able to use my language crafting skills as a lawyer to build structured legal judgment into the workflow itself. It grew from there. We added different analysis modes for clients and lawyers with faster outputs and escalation recommendations for clients, and deeper review and both-sides arguments for lawyers and integrated external data sources. When I handed the workflow off to the team, they started using it right away and asked Copilot to do more. That foundation has since evolved into a full desktop app for running predefined legal workflows in a clean interface. Building the desktop app required writing some code a lot of code, actually , but the core instructions used to customize workflows are easily edited and customized in the app using plain language. The app we created has now expanded well beyond only code analysis for DMCA notices. It includes instructions for many in-house workflows like contract review, NDA triage, risk assessment, compliance checks, and response drafting. Under the hood, it can route work through reusable skills and agents intake, playbook alignment, risk scoring, evidence verification, escalation routing, report assembly , but the important part is not technical complexity—it’s that legal teams can still control behavior in readable Markdown. For me, the key lesson was that I don’t need to wait for the perfect software vendor—or become a full-time developer myself—to build serious AI tooling. If you can clearly define your methodology, your standards, and your output format, GitHub Copilot makes it easy to operationalize that knowledge. My legal Copilot is not a replacement for legal judgment, and it shouldn’t be treated that way. It’s a structured decision-support system designed to keep human review central while making legal analysis more consistent, more transparent, and more scalable. Take this with you If a team of lawyers and program managers can quickly build their way out of the hard days, so can you. Many jobs, even those not centered on traditional software development, have repetitive tasks that can be automated. Pick one thing that slows you down, open Copilot CLI, and ask it to help you build a fix. Tags: Written by Related posts Stacked sessions and pull requests in the GitHub Copilot app https://github.blog/ai-and-ml/github-copilot/stacked-sessions-and-pull-requests-in-the-github-copilot-app/ Learn how I modernized an old codebase of mine using stacked sessions and pull requests in the GitHub Copilot app. The harness is all you need mostly https://github.blog/ai-and-ml/github-copilot/the-harness-is-all-you-need-mostly/ A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool. GitHub Copilot app for Beginners: Getting started https://github.blog/ai-and-ml/github-copilot/github-copilot-app-for-beginners-getting-started/ New to the GitHub Copilot app? 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