How to Set Up an AI Coding Agent in Claude or Cursor (Step-by-Step)
Most developers already use Claude or Cursor, but many still use them like slightly better autocomplete: open a chat, paste a task, then re-explain the stack, the quality bar, the testing rules, and the repo constraints every time. That is not an AI coding agent setup. That is repetitive prompt babysitting.
A real AI coding agent setup has durable instructions. You give the model a stable operating mode once, store it in a reusable config, and then use short task prompts for the changing input. At SkillPack we usually package that pattern as a SKILL.md-style configuration: one file for role, context, workflow, and standards.
This guide shows the practical version for developers. You can use it whether you prefer Claude or Cursor. If you want the shortcut instead of building from scratch, start with the free Coding Agent generator or go straight to the Coding Agent Pack.
Step 1: Stop Thinking in Prompts, Start Thinking in Operating Modes
The biggest mistake is treating every request like a fresh conversation. Software work is too variable for that. A feature plan, a code review, and a production bug all need different behavior. If the model has no default mode, it improvises.
Your first move is to define the role you want the AI to play most often. For example: senior full-stack developer, code reviewer, debugging specialist, or feature planner. The more specific the role, the less generic the output.
Minimum useful skeleton
Role: Senior TypeScript engineer for this repository Goal: Ship safe, maintainable changes with tests Priorities: correctness, regression risk, small blast radius Output: findings first, plan second, code third
Step 2: Add Project Context Where Claude or Cursor Will Actually Reuse It
Put the durable instructions in the place your tool can consistently carry forward. In Claude that usually means project-level instructions or an attached reference file. In Cursor that usually means project rules plus repository files the agent can read. The exact UI may change, but the principle does not: stable context belongs in a reusable home, not inside your daily task message.
Good coding agent context includes the stack, architecture boundaries, test command, deployment constraints, code review standard, and anything the AI must never assume. If your repo already has `AGENTS.md`, `README.md`, or ADRs, reference them explicitly. Those files tell the model how this codebase behaves instead of how an average codebase behaves.
Rule of thumb:the task prompt should explain today's problem. The config should explain the engineering environment the AI works inside every day.
Step 3: Encode Workflow Rules, Not Just Tone
Many developers stop after writing a polite system prompt. That is not enough. The useful part of an AI coding agent setup is workflow logic: how it should investigate, how it should prioritize risk, and how it should communicate uncertainty.
For example, you might tell the agent to inspect existing patterns before inventing new abstractions, to prefer the smallest safe fix, to call out missing tests, and to cite file references when reviewing code. Those rules create consistent behavior across many tasks.
- Read the relevant files before proposing a solution.
- State assumptions when the repo does not prove a fact.
- Prioritize correctness and regression risk over style.
- Prefer small, testable changes over sweeping rewrites.
- List verification steps before calling the task complete.
Step 4: Split Reusable Modes Instead of Building One Giant Prompt
One monolithic instruction file usually gets blurry. A better pattern is to keep a small base config and then create a few specialist modes underneath it: code reviewer, debugger, feature planner, and implementation agent. That mirrors how senior engineers actually switch mental models.
If you want to see this setup in more detail, the SKILL.md setup guide walks through how to reuse the same config across tools. The important part is keeping the stable rules in files and the variable task in the prompt.
This is also why the Coding Agent Pack ships multiple workflows instead of one mega-instruction. Review, planning, debugging, and implementation each need different defaults.
Step 5: Run One Real Task and Tighten the Defaults
Your first live task should be something ordinary, not dramatic: review a pull request, fix a contained bug, or plan a small feature. Watch where the agent guesses wrong. That is where your config is thin.
If the model proposes code too quickly, add an investigation rule. If it writes patches without tests, add a verification rule. If it ignores repo conventions, add file-level references the tool should inspect first. Small corrections here create compounding gains later.
That is the whole setup loop: define the role, store project context, encode workflow rules, split specialist modes, then tighten after real use. If you want a faster starting point, generate a draft at /generate/coding-agent or unlock the ready-made Coding Agent Pack.
Want a Coding Agent that starts from better defaults?
Generate a free customized preview first, then upgrade to the full Coding Agent Pack when you want the complete expert workflow library.