Step-by-step GitHub Copilot workflows from How I AI expert interviews. Each guide includes prompts, code snippets, and practical tips.
Use a Codex Automation to create a 'meta-agent' that analyzes your codebase, identifies opportunities for new reusable agent skills, and then spawns sub-agents to automatically test and validate those new skills.
Create an AI agent in Claude Code to automatically review aging pull requests, prompt your team for action via Slack, and even fix issues autonomously. This routine helps engineering teams ship code faster and reduce manual follow-up.
Ensure UI consistency across projects by using AI to analyze your codebase and generate a 'living design system' in a single HTML file. This artifact acts as a portable, visual source of truth for you, your AI, and your team.
Shift from writing implementation code to writing human-readable specifications. Write a detailed spec in a markdown file, and let an AI agent handle the entire process of coding, testing, and verification, using the spec as the single source of truth.
Implement bug fixes and small features rapidly by creating a detailed task in Notion and @mentioning a coding agent. The agent reads the task, writes the code, and submits a complete pull request with a live preview link, drastically reducing friction.
Save 20+ minutes daily by creating a custom Notion agent that automatically gathers updates from Slack, GitHub, and Honeycomb to generate a comprehensive standup pre-read document. This keeps your team aligned and focused on problem-solving, not reporting.
Create or use an automated script that runs daily on your personal knowledge base (like Obsidian) to fix typos, enrich notes with research, create structure, and cross-link related ideas.
Gamify AI adoption by building an internal platform where employees can post 'Quests' for automation needs and builders can team up to create solutions, earning rewards and recognition.
Turn your team's standard operating procedure for shipping code into an executable AI skill. Automate running tests, fixing lint, pushing to the repo, and monitoring CI, saving significant engineering time and reducing friction.
Build a custom `/deploy` command that automates the entire Git process—from branching and committing to creating a pull request and monitoring CI checks. The AI can even fix its own CI failures.
Create an automated system that uses an AI agent to convert Figma designs into Next.js code. The agent then verifies its output against the original design and iteratively corrects its own mistakes.
Learn how to use Vercel's v0 to add new features to a live application. This workflow covers branching, prompting, iterating, and creating a pull request, all within the v0 interface, streamlining the entire development process from idea to production.
Use Spec Kit with an AI assistant like GitHub Copilot to improve the quality of your software specifications. The AI acts as an intelligent partner, analyzing your requirements and asking clarifying questions to help you identify edge cases and ambiguities before development begins.
Create an automated agent that analyzes interview transcripts against a job description and company values to provide an unbiased hiring recommendation. This system acts as a consistent thought partner to reduce bias and improve decision-making speed.
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