AI for Coders
Write code? Answer a few questions and get a developer setup plan: the right AI coding tool for your work, the exact install command for your OS, a ready-to-paste project-rules file (CLAUDE.md / AGENTS.md), and how to match the model to the job.
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We use this only to recommend an assistant that actually works where you are, since some AI tools are blocked in certain countries. We detect it automatically; change it if it's wrong.
Question 1
Last verified:
Pick the tool, then set it up like a pro
Choosing an AI to help you ship code is its own thing. The tool that's right for terminal agents isn't the one that's right inside your editor, and the install differs by operating system. Answer a few questions (what you're building, where you want the AI to work, how big your repo is, your OS) and you get a concrete developer setup: the tool that fits, the exact install command, a ready-to-paste project-rules file, and the context tools worth adding when your codebase is large.
The point isn't to crown one tool. They're all good; what differs is how well each matches your surface and your workflow. Everything starts on a free or trial tier, and the result tells you plainly when the work genuinely outgrows it.
Four things that change how much AI helps you
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A project-rules file is the highest leverage
A short
CLAUDE.mdorAGENTS.mdat your repo root, naming the stack, the build and test commands, the conventions and the hard "never do this" rules, is the single biggest lever on output quality. The agent reads it before every task, so it stops guessing. This tool generates a starter you can paste in and edit. -
Manage the agent like a junior dev
Give it small, scoped tasks, write the constraints down, and review every diff before you accept it. The teams that get the most from coding agents treat them like a fast, capable junior: clear instructions in, careful review out, work on a branch.
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Match the model to the job
Reach for an Opus-class model on a complex refactor or a hard architecture call; lean on a faster GPT or Codex-class model for tight agent loops you run many times; use a long-context Gemini-class model when you need to search a very large codebase. The frontier model by default just burns time and budget.
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Don't reach for MCP first; context discipline wins
Most of what people set up an MCP server for, a plain CLI command does more simply. And a focused, well-curated context beats a giant one: feed the agent the few files that matter, not the whole tree. Bigger context windows are not a substitute for context discipline.
Same maintained data, a developer's lens
The tool recommendations run on the same maintained data as the rest of Skuto: the capabilities matrix behind the AI chooser and the price list behind the plan picker, both straight from the vendors' own pages and re-read by an automated scan every week. The "Last verified" date below only moves when we actually re-checked. No vendor pays to appear, and no amount of money moves a ranking. Once you're set up, the prompt fixer can sharpen the instructions you hand the agent.
AI coding tools ship changes weekly: install paths, model names and free-tier limits all drift. What you see here was correct as of the date below; if an install command has changed, the tool's own docs (linked in the result) are the source of truth. The full process, sources included, is on our how we test page.
Frequent questions
Which AI coding tool should I use? +
There's no single best one; there's a best fit for how you work. For agentic work in the terminal, Claude Code and the OpenAI Codex CLI lead; inside an editor, Cursor and the GitHub Copilot agent are the natural picks; for a genuinely huge codebase, a long-context model like Gemini earns its place. This tool asks what you're building, where you want the AI to work and how big your repo is, then names the tool that fits and shows the exact install for your operating system.
What is a CLAUDE.md or AGENTS.md file, and do I need one? +
It's a short project-rules file the agent reads before it touches your code: the stack, the commands to build and test, the conventions to follow and the things never to do. It is the single highest-leverage thing you can set up, because it turns vague guesses into informed ones on every request. The tool generates a starter scaffold for your tool (CLAUDE.md for Claude Code, AGENTS.md for Codex and others) that you copy into your repo root and edit.
How do I match the model to the job? +
Use the heavy model only where it pays off. A complex refactor or tricky architecture call wants an Opus-class model; fast agent loops where you iterate many times lean toward a GPT or Codex-class model that is quick and cheap; a search across a very large codebase wants a long-context Gemini-class model. Most day-to-day editing does not need the frontier model at all, and reaching for it by default just burns time and budget.
Do I need MCP servers to get value? +
No, and reaching for MCP first is a common trap. Most of what people set up an MCP server for, a plain CLI command in your project-rules file does more simply and more reliably. Get the basics right first: a good CLAUDE.md / AGENTS.md, small scoped tasks and reviewing diffs. Add an MCP server only when there's a specific integration a CLI genuinely can't cover.
Is it safe to let an AI agent edit my codebase? +
Treat the agent like a capable but junior developer. Give it small, scoped tasks, write the constraints down, and review every diff before you accept it; never let it run on sensitive code or production without a human reading the change. Work on a branch, keep commits small, and you keep all the speed with none of the "what did it just do" surprises.
Tools you might need next
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