Cursor’s Coding Agents: Plan Mode, Skills, and Reliable Workflows

A new guide from Cursor distills practical patterns for making AI coding agents reliable collaborators, from Plan Mode file-by-file planning to persistent project rules and Skills. It’s a quick playbook for teams adopting agent-driven development without sacrificing code quality.

Cursor’s Coding Agents: Plan Mode, Skills, and Reliable Workflows

TL;DR

  • Plan Mode (Shift+Tab): Drafts file-by-file implementation plans, asks clarifying questions, waits for approval, and saves plans to .cursor/plans/ for resuming work.
  • Rules and Skills for customization: Persistent project rules in .cursor/rules/RULE.md; Skills in SKILL.md expose custom commands, hooks, domain logic, and long-running loop patterns; Skills currently on the nightly channel.
  • Context management: Uses grep and semantic search to fetch relevant files on demand; @Past Chats references prior interactions without duplicating histories.
  • Review and parallelism: Real-time interruption during generation, post-run Agent Review of diffs, and parallel agents in isolated git worktrees for multi-model comparisons.
  • Cloud agents and Debug Mode: Cloud agents run in remote sandboxes and can open PRs; Debug Mode follows hypothesis → instrumentation → runtime data → targeted fixes.
  • Common workflows and integrations: TDD-first testing, reusable structured git commands, verifiable goals (linters, typed languages, tests), and integrations (Figma MCP, browser control, hooks).

Guide to Cursor’s coding agents — a concise overview

Cursor’s guide lays out practical patterns and workflows for using AI-powered coding agents, focused on what developers need to know to make agents reliable collaborators. It frames the agent harness around three core parts — instructions, tools, and user messages — and then describes how planning, context, and extendability shape successful outcomes.

What stands out (two key reasons to read the full guide)

  • Plan Mode (Shift+Tab): Agents can draft a file-by-file implementation plan, ask clarifying questions, and wait for approval before making edits. Plans are savable to .cursor/plans/ to preserve context and resume interrupted work. This workflow changes how to approach multi-file refactors and larger features.
  • Rules and Skills for customization: Persistent project rules live in .cursor/rules/RULE.md, while dynamic capabilities live in SKILL.md. Skills can expose custom commands, hooks, and domain knowledge — including patterns for long-running agent loops (for example, iterating until tests pass). Agent Skills are currently available via the nightly channel.

Other practical highlights

  • Context management: The agent leverages grep and semantic search to fetch relevant files on demand, avoiding the need to enumerate every file in prompts. Use @Past Chats to reference previous work without duplicating long histories.
  • Review and parallelism: Real-time interruption is supported during generation; post-run Agent Review analyzes diffs. Parallel agents run in isolated git worktrees, enabling multi-model comparisons and side-by-side judging of solutions.
  • Cloud agents and Debug Mode: Cloud agents work autonomously in remote sandboxes and can open PRs when finished. Debug Mode follows an evidence-driven approach: hypothesis generation, instrumentation, runtime data collection, and targeted fixes.
  • Common workflows: Recommended patterns include TDD (write failing tests first), structured git commands saved as reusable commands, and using verifiable goals (linters, typed languages, tests) to guide agent iteration.

The guide mixes concrete editor shortcuts, repository conventions, and integration points (Figma MCP, browser control, hooks) to help teams adopt agent-driven development without losing code-quality standards.

Read the full guide at the original source: Cursor — Guide to Cursor’s Coding Agents

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