Github-Copilot on Corey Daley

Centralizing AI Agent Configurations with the agent-config Repository

Thu, 19 Feb 2026 20:26:46 -0500

If you’re juggling Claude Code, Codex, and GitHub Copilot, you know the pain of keeping each one’s configuration files in sync. My agent-config repo solves that with a single source of truth: agent instructions, reusable skills, custom commands, subagents, and prompts all live in one place, and a single make symlinks command wires them up across every tool.

Each directory is purpose-built—skills for reusable capabilities, commands for CLI tools, subagents for delegation, prompts for task-specific guidance. The setup even backs up any files it would overwrite, so you never lose existing config.

Whether you’re just starting to extend your AI tools or already deep into custom workflows, having everything version-controlled and centralized is a game changer. Are you managing your AI agent configurations in a single repository, or do you keep them scattered across tools?

Finding Each AI's Place in My Workflow

Thu, 12 Feb 2026 19:28:28 -0500

I’ve stopped trying to pick the ‘best’ AI tool—instead, I’m letting each one find its place in my workflow. Here’s what’s emerged: Codex, Claude Code, and GitHub Copilot CLI handle my command-line coding from simple to complex. ChatGPT web is my go-to for image creation (oddly, ChatGPT Desktop lacks this). GitHub Copilot in VSCode crushes code completion. Claude Code and Claude Desktop excel at blog writing with Notion integration.

Each tool has found its niche, and I’m more productive because of it. I’m still exploring how to use AI as a peer for bouncing ideas off, especially in planning mode.

The future isn’t about one AI to rule them all—it’s about orchestrating multiple specialists. How are you integrating AI tools into your workflow? Have you found similar specialization patterns, or are you using a different approach?

The Art of Iterative Cycles with AI: Why Your First Prompt is Never Your Best

Wed, 04 Feb 2026 19:50:00 -0500

Here’s something I’ve learned about AI coding assistants: the first response is rarely perfect—and that’s actually a good thing. When I started using GitHub Copilot and Claude, I expected instant perfect code. Reality? AI interprets your instructions based on patterns and context, so the first attempt is often close but not quite right.

The key is treating AI like a junior developer: start with clear instructions, review the result, provide feedback, and iterate. Each cycle gets closer to what you need. This isn’t a limitation—it’s how effective collaboration works.

The developers who succeed with AI aren’t the ones with perfect prompts; they’re the ones who embrace refinement. Have you experienced this iterative dance with AI tools? How many rounds does it usually take you to get the result you want?

Managing Blog Posts with GitHub Copilot

Wed, 04 Feb 2026 16:25:00 -0500

Managing a blog used to mean juggling multiple tools—notes apps for ideas, editors for drafting, task trackers for progress. It was fragmented and exhausting. Then I discovered GitHub Copilot can work directly with GitHub Issues and Projects, creating a seamless workflow.

Here’s how it works: I create issues for blog post ideas, GitHub Copilot reads the issue, generates the complete post with proper frontmatter and content, and automatically closes the issue when I publish. No context switching, no lost ideas, just a smooth pipeline from concept to publication. The best part? Everything lives in one place alongside my code.

If you’re blogging and using GitHub, this workflow is a game-changer. How are you managing your content pipeline? Have you tried integrating your blog workflow with your code repository?

Building with AI: Copilot and Claude

Tue, 03 Feb 2026 21:02:25 -0500

Building websites has changed dramatically—I’m no longer staring at code for hours. Instead, I’m collaborating with Claude and GitHub Copilot to build this Hugo blog. Here’s what I’ve learned: Claude is my architect. When I need structural changes or new layouts, Claude generates complete solutions and explains every decision.

GitHub Copilot is my coding assistant, finishing my thoughts as I type and handling routine tasks. Together, they create a powerful workflow where Claude handles the big picture and Copilot speeds up execution. The result? I spend less time debugging and more time creating.

If you’re curious about AI-assisted development or wondering which tool does what, this post breaks down how they complement each other. Are you using AI tools in your workflow? How do you divide the work?