How to use Claude to generate higher-quality code
This article was inspired by Boris Tane. I've gained so much from it and am incredibly grateful for his selfless sharing.
I used to fall into the 'prompt-plan-fix' cycle, but I found that for complex tasks, the results quickly become a mess. It just doesn't scale for anything beyond simple scripts.
This skill enforces a disciplined pipeline that separates thinking from typing
If you want to generate higher-quality code that aligns with your goals, you should use this skill.
Now, to see how this actually performs in the wild, I ran a few tests. Here’s a breakdown of the results and what they mean.
Experiments
Three tasks were run on the same codebase, comparing with-skill vs. without-skill.
- Eval 1: Add comprehensive error handling (cross-cutting, 5+ files)
- Eval 2: Add caching for expensive operations (moderate, 2 files)
- Eval 3: Fix flaky tests (debugging, 1-2 files)
Code Quality
| Metric | Eval 1 | Eval 2 | Eval 3 | |
|---|---|---|---|---|
| Read:Write Ratio | With | 7.0 | 4.3 | 4.5 |
| Without | 1.0 | 1.4 | 3.5 | |
| Exploration Breadth | With | 14 | 13 | 9 |
| Without | 13 | 5 | 6 | |
| Reverts | With | 0 | 0 | 0 |
| Without | 0 | 0 | 0 | |
| Assertions | With | 5/5 | 5/5 | 5/5 |
Read:Write Ratio is how many files the agent read versus how many it changed. Think of it as "how much did it study before it started writing?" With the skill, the ratio is consistently 4-7x — it reads a lot before touching anything. Without the skill, Eval 1 hit a 1:1 ratio, meaning the agent was editing files the moment it started reading them. That's like writing code before you understand the system.
Exploration Breadth counts how many Read/Glob/Grep calls happened before any code was written. Look at Eval 2 — the with-skill agent made 13 exploration calls, the without-skill agent only made 5. That means the without-skill agent skipped checking whether caching patterns already existed in the codebase. It got lucky there were none, but it never actually verified that.
Reverts counts how many times the agent had to undo its own work. Zero across the board — the skill got it right on the first pass every time.
Assertions are task-specific quality checks like "did it research before planning?" and "did it build on existing patterns instead of duplicating them?" 15 out of 15 passed.
Usage
Install
Project scope (available only in the current project):
# Clone the repo to a temp location
git clone https://github.com/meatballg1210/tane-flow.git
# Create the skills directory in your project
mkdir -p .claude/skills
# Move to the skill folder
mv tane-flow .claude/skills/
Global scope (available across all your projects):
# Clone the repo to a temp location
git clone https://github.com/meatballg1210/tane-flow.git
# Create the global skills directory
mkdir -p ~/.claude/skills
# Move to the global skill folder
mv tane-flow ~/.claude/skills/
That's it. Claude Code picks up skills automatically from .claude/skills/ (project) or ~/.claude/skills/ (global).
How to Use
The skill triggers automatically when you ask for multi-file changes, architectural refactors, new features, performance work, or anything that could conflict with existing codebase patterns. Just describe your task normally:
Add caching to improve performance
You can also trigger it explicitly with /tane-flow:
/tane-flow Refactor the error handling system