You now know how to get Claude Code running and give it project context. The real question is what to do with it. Claude Code handles a wide range of development tasks, from the routine grunt work you avoid to complex multi-file features.
The tedious stuff you keep putting off
Start with the tasks that pile up in your backlog. Claude Code excels at work that's necessary but mind-numbing.
Writing tests is the classic example. Point Claude Code at untested functions and it writes comprehensive test suites, runs them, and fixes any failures it finds:
claude "write tests for the auth module, run them, and fix any failures"
Fixing lint errors across an entire project takes minutes instead of hours. Claude Code processes every file, applies consistent fixes, and handles edge cases you'd spend time researching.
Updating dependencies becomes straightforward. Claude Code checks for breaking changes, updates import statements, and fixes compatibility issues across your codebase.
Writing release notes from git commits. Claude Code reads through your commit history and generates readable summaries organized by feature, fix, and breaking change.
Building features and fixing bugs
Claude Code handles substantial development work, not just cleanup tasks.
Describe a feature in plain language and Claude Code plans the implementation, writes code across multiple files, and verifies everything works together. It understands your project structure and follows your existing patterns.
For bug fixes, paste an error message or describe what's broken. Claude Code traces the issue through your codebase, identifies the root cause, and implements a fix. It reads stack traces, follows function calls, and understands how different parts of your system interact.
The key difference from other AI coding tools: Claude Code maintains context across your entire project. When fixing a bug in your API, it knows to update the corresponding frontend code and tests.
Git and code review workflows
Claude Code works directly with git. It stages changes, writes descriptive commit messages, creates branches, and opens pull requests:
claude "commit my changes with a descriptive message"
The commit messages aren't generic. Claude Code reads your changes, understands the purpose, and writes messages that help your team understand what happened and why.
For code review, Claude Code can analyze pull requests, flag potential issues, and suggest improvements. Run it in CI to catch problems before human reviewers see them.
Connecting external tools
The Model Context Protocol (MCP) lets Claude Code work with your existing tools. It can read design documents from Google Drive, update tickets in Jira, pull conversation history from Slack, or integrate with custom internal tools.
This matters for real workflows. Instead of copying requirements from Slack into your terminal, Claude Code reads them directly. Instead of manually updating tickets after deploying a fix, Claude Code handles the status changes.
Automation and scheduling
Claude Code runs on schedules to handle recurring work. Morning PR reviews, overnight CI failure analysis, weekly dependency audits, or syncing documentation after merges.
These routines run on Anthropic's infrastructure, so they keep working when your computer is off. Create them from the web interface, desktop app, or command line.
You can also pipe other tools into Claude Code for automated analysis:
tail -200 app.log | claude -p "Slack me if you see any anomalies"
git diff main --name-only | claude -p "review these changed files for security issues"
This composability makes Claude Code useful for CI/CD pipelines and monitoring systems.
What works best
Claude Code excels at tasks that require understanding your entire codebase. Cross-file refactoring, feature implementation that touches multiple components, and debugging issues that span different parts of your system.
It's also excellent for tasks you understand but don't want to do manually. The pattern recognition and execution speed make it ideal for repetitive work that still requires intelligence.
The areas where it struggles: complex architectural decisions that require deep business context, performance optimization that needs profiling data, and tasks that require specialized domain knowledge your codebase doesn't contain.
Start with the straightforward automation wins. Write those missing tests, fix those lint errors, clean up that technical debt. Once you see how Claude Code handles routine tasks, you'll recognize opportunities for more complex automation in your daily workflow.