There's a version of Claude Code that writes decent boilerplate when you describe what you need in broad strokes. Then there's a version that operates more like a disciplined contractor - one who knows exactly what "done" looks like before starting, and breaks complex jobs into managed sub-tasks. The difference isn't which model you're running. It's two habits.
Define "Done" Before You Start
Most prompts describe what to do. Effective ones also specify how you'll know if it worked.
"Success criteria" means telling Claude Code not just what to build, but what the finished result needs to satisfy. Instead of "write a script that processes customer CSV files," you write: "write a script that processes customer CSV files - success means it handles empty rows without crashing, outputs a summary count at the end, and completes in under 10 seconds on a 50,000-row file."
This sounds obvious, but most people skip it. When you skip it, you often get technically-correct code that doesn't do what you actually needed. The model is guessing at your definition of done, and it's frequently wrong in ways that cost you debugging time. Defining success criteria upfront forces you to think through what you actually want - and gives the agent something concrete to verify against rather than just finishing and moving on.
Subagents Work Better When You Direct Them
Claude Code can spawn sub-tasks - smaller, focused agents that handle specific pieces of a larger job. Most people let this happen accidentally. The better approach is to structure it intentionally.
The practical difference: if you're building something with three moving parts, you can hand Claude Code one big prompt and hope it keeps track of everything. Or you can structure it so each part gets its own focused scope - the agent handling the database schema isn't also juggling the API design and the frontend spec in memory at the same time.
Subagents (also called agentic sub-tasks depending on the interface) let Claude Code parallelize or sequence work with clearer boundaries. The output is more predictable and easier to review because each piece has a defined scope with a defined finish line.
Build Skills and Docs After You Know What Repeats
One common mistake: investing time in custom instruction sets and .md reference documents before you've actually used the tool enough to know which patterns will repeat. Building an elaborate Claude Code configuration on day one usually produces a setup that fits your guesses about how you'll work, not how you actually do.
The more useful approach is to use Claude Code plainly for a while. When you notice yourself giving the same type of instructions repeatedly - same context, same constraints, same expected output format - that's the signal to document it. At that point, a skill or .md file is solving a real problem you've already observed, not a hypothetical one.
The underlying principle: your effectiveness with AI coding tools is a product of output quality and the scale at which you can sustain it. Success criteria raise quality. Intentional subagent use raises scale. Skills and docs reduce friction once you know what friction actually looks like - not before.