Why I Chose GitHub Copilot Over Claude Code

I've been experimenting with both GitHub Copilot and Claude Code, and I haven't been sure which one to actually use every day. Both have their strengths, but maintaining two separate ecosystems of instruction files and skills is a headache.

The Double Maintenance Problem

Running both tools meant maintaining two separate instruction files—one for Copilot, one for Claude Code. Every time I wanted to refine how the AI understood my codebase, I had to update both. Every coding pattern, every project-specific convention, duplicated.

As a solo dev I don't have the bandwidth for that kind of overhead. I needed to pick one and go deep.

The Linting Error That Decided It

The breaking point came when I had a JSX syntax error in my SEO optimization plan document. Line 164 had a malformed closing tag—I'd written }} /> instead of }</CodeBlock>.

Claude Code got stuck. It kept regenerating sections, suggesting fixes that didn't quite work, going in circles.

GitHub Copilot ran get_errors, saw the exact error at line 164 with full TypeScript diagnostics, and surgically replaced just that one line. Done in seconds.

Precision vs. Generation

After that experience and some research, the pattern became clear:

• GitHub Copilot excels at: Fine-tuned IDE precision edits, surgical debugging with exact line numbers, targeted refactors, context-aware fixes with deep VS Code integration
• Claude Code excels at: Larger scale code generation, architectural brainstorming, greenfield project scaffolding, drafting entire features from scratch

My workflow is 90% precision edits and incremental improvements. I build features one at a time, test them, commit, move on. I'm not asking an AI to generate an entire enterprise app—I'm asking for help fixing bugs, adding components, understanding errors.

IDE-Native Work

Copilot sees everything VS Code sees: linting errors with exact line numbers, TypeScript diagnostics, git state, terminal output, workspace structure. It can read files, edit them surgically with context matching, run commands with proper escaping.

That means no back-and-forth copying code into a chat interface. No asking the AI to regenerate an entire function when you only needed one line changed. No lost context between conversations. When I'm working through a feature checkpoint-by-checkpoint, Copilot stays in sync with my actual codebase state.

Solo Dev Realities

I'm not an "agent superuser" running 100 autonomous agents in parallel. I maintain architectural control. I think through features carefully. I make intentional changes and verify them before moving forward.

For that style of work, Copilot is the better tool. It's a precision instrument, not a code generator that might veer off in random directions.

The Skills Investment

Beyond just instruction files, I have begun building custom skills—Claude skills or Copilot Agent Skills. That's a significant time investment, and I can only maintain one ecosystem well.

Since Copilot is already embedded in my daily workflow and has proven more reliable for the kind of work I do, that's where I'm investing my effort.

What Claude Is Still Good For

I'm not abandoning Claude entirely. The conversational interface is still valuable for:

• Brainstorming architecture for new features
• Generating initial drafts of complex documents
• Thinking through design problems at a high level

But for the daily cycle of "fix this bug, add this feature, debug this error," Copilot has proven more reliable and faster.

The Decision

Consolidating on GitHub Copilot and Github Copilot SDK. One instruction file to maintain, one skill ecosystem to build in, one tool that fits my incremental, checkpoint-based workflow.