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AI Fluency Resources by

Browse our AI coding resources by what you want to do — getting started, writing and refactoring code, testing, code review, databases, debugging, deployment, or automation. Each use case has a curated set of tools, MCP servers, and cheatsheets, with first-hand notes on when to reach for each.

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8 use cases

6 resources

Getting started with AI coding agents

How to start coding with AI agents: pick one all-in-one tool, connect the filesystem and GitHub MCP servers, and work in plan mode first.

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8 resources

Writing and refactoring code with AI

The best AI tools for writing and refactoring code — terminal agents, AI editors, and open-source assistants — and how to get reliable results.

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1 resource

AI tools for testing and QA

How to test with AI agents: the Playwright MCP server for browser/E2E control, paired with an agent that runs your suite as its verification step.

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1 resource

AI tools for code review and pull requests

Use the GitHub MCP server so an AI agent can read PRs, inspect failing CI, and propose fixes — turning copy-paste-from-CI into a single step.

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2 resources

AI tools for working with databases

Work with Postgres from an AI agent using the Supabase MCP server and agent skills — with the staging-first discipline that prevents costly mistakes.

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1 resource

AI tools for debugging and fixing errors

Debug production errors with AI: the Sentry MCP server pulls real issues and stack traces into the session so the agent fixes against the actual signal.

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1 resource

AI tools for deployment and shipping

Close the loop between a push and knowing it shipped: the Vercel MCP server lets an agent read deployment state and build logs.

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3 resources

Automating workflows with AI agents

Automate repeatable work with Agent Skills — reusable, model-invoked bundles of instructions and scripts — orchestrated by Claude Code.

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