Best practices for Claude Code
by Anthropic
Anthropic's field guide to working effectively with an agentic coding tool.
Overview
Best practices for Claude Code is Anthropic's official documentation on getting the most out of Claude Code, its agentic coding environment. Framed around one core constraint — the context window fills fast and model performance degrades as it fills — the guide lays out concrete, battle-tested patterns: give the agent a check it can run (tests, build exit codes, linters, screenshot diffs) so it can close its own verification loop; separate exploration and planning from implementation using plan mode (explore → plan → code → commit); write a concise, high-signal CLAUDE.md and prune it ruthlessly; configure permission allowlists, hooks, skills, subagents, MCP servers, and plugins to extend the tool; and manage context aggressively with /clear, /compact, checkpoints, and investigation subagents. It also covers scaling up: non-interactive/headless mode (claude -p) for CI and scripts, running multiple parallel sessions with git worktrees, fan-out migrations across many files, auto mode for autonomous runs, and adding an adversarial subagent review step before calling work done. A closing section on common failure patterns (the kitchen-sink session, over-correcting, an over-specified CLAUDE.md, the trust-then-verify gap) makes it a practical reference for anyone building with agentic coding tools.
At a Glance
- Topic
- Agentic
- Level
- All Levels
- Format
- Documentation
- Cost
- Free
- Duration
- ~25 min read
- Provider
- Anthropic
- Hands-on
- No
- Certificate
- None
What You’ll Learn
- ✓How to give an agent a verification signal (tests, builds, screenshots) so it self-corrects
- ✓The explore → plan → code → commit workflow and when to skip planning
- ✓Writing and pruning an effective CLAUDE.md, plus when to use skills, hooks, and subagents instead
- ✓Managing context aggressively with /clear, /compact, checkpoints, and investigation subagents
- ✓Scaling with headless mode, parallel worktree sessions, fan-out migrations, and adversarial review
Highlights
- •Official Anthropic guidance drawn from internal team usage
- •Actionable before/after prompt tables for scoping tasks
- •Covers agentic engineering patterns applicable beyond Claude Code (context engineering, verification loops)
- •Explicit catalog of common failure modes and their fixes
Who It’s For
Best For
- ✓Developers and AI engineers adopting agentic coding tools
- ✓Teams standardizing on CLAUDE.md, hooks, and subagent workflows
- ✓Engineers automating Claude in CI, scripts, and large migrations
Prerequisites
- •Basic software development workflow (git, tests, CLI)
- •Helpful: hands-on time with Claude Code or a similar agentic coding tool
FAQ
What is Best practices for Claude Code?
Anthropic's official best-practices guide for Claude Code, distilling patterns proven across Anthropic's internal teams and external engineers. It shows AI engineers and developers how to manage context, structure the explore-plan-code-commit loop, give the agent a way to verify its own work, and scale to parallel and headless sessions.
Is Best practices for Claude Code free?
Best practices for Claude Code is free to access.
What level is Best practices for Claude Code for?
Best practices for Claude Code is aimed at a all levels audience. Recommended background: Basic software development workflow (git, tests, CLI), Helpful: hands-on time with Claude Code or a similar agentic coding tool.
How long does Best practices for Claude Code take?
Expect roughly ~25 min read. Most learners work through it at their own pace.
What will I learn from Best practices for Claude Code?
You'll learn: How to give an agent a verification signal (tests, builds, screenshots) so it self-corrects; The explore → plan → code → commit workflow and when to skip planning; Writing and pruning an effective CLAUDE.md, plus when to use skills, hooks, and subagents instead; Managing context aggressively with /clear, /compact, checkpoints, and investigation subagents; Scaling with headless mode, parallel worktree sessions, fan-out migrations, and adversarial review.