I Used to Dread Writing Tests and Code Reviews. Now AI Does the Heavy Lifting.

Get the tool: unit-testing

Also check out: comprehensive-review

The Confession

I have a confession. For years, I treated testing like flossing - I knew it was important, I agreed it was a best practice, and I absolutely did not do it as often as I should.

It's not that I didn't understand the value. I've felt the pain of shipping bugs that a single unit test would have caught. I've stared at legacy code wondering what it was supposed to do, wishing past-me had written tests as documentation. I've been that developer nodding along during discussions about TDD while secretly thinking "yeah, but who has time?"

The honest truth? Writing tests felt like extra work. The feature was done. It worked (on my machine). And now I had to write a bunch of boilerplate to prove what I already knew?

Then I discovered the unit-testing plugin for Claude Code. And something shifted.

What Changed

The unit-testing plugin comes with two agents and a command that fundamentally changed how I think about testing:

The test-automator agent - An AI test automation engineer that understands modern frameworks, TDD practices, and generates comprehensive test suites.

The debugger agent - A specialist in root cause analysis for test failures and unexpected behavior.

The

command - Analyzes your code and creates ready-to-run tests with proper mocking and edge case coverage.

Here's the thing that shifted my perspective: I wasn't skipping tests because I'm lazy. I was skipping tests because the activation energy was too high. Figuring out what to mock, remembering the assertion syntax, covering edge cases I hadn't thought of - by the time I'd done all that, my momentum was gone.

The unit-testing plugin removes that friction entirely.

Test Generation That Actually Works

The

command scans your code and creates comprehensive test suites. It's not just slapping together basic assertions - it analyzes your functions, identifies edge cases, and generates proper mocks for dependencies.

Here's what it produces for a Python function:

pythonCopy# Your code
def calculate_discount(price: float, user_tier: str) -> float:
    """Apply discount based on user tier."""
    discounts = {"bronze": 0.05, "silver": 0.10, "gold": 0.15}
    if price < 0:
        raise ValueError("Price cannot be negative")
    return price * (1 - discounts.get(user_tier, 0))

# Generated tests
import pytest
from your_module import calculate_discount

class TestCalculateDiscount:
    """Test suite for calculate_discount function."""

    def test_bronze_tier_applies_five_percent_discount(self):
        assert calculate_discount(100.0, "bronze") == 95.0

    def test_silver_tier_applies_ten_percent_discount(self):
        assert calculate_discount(100.0, "silver") == 90.0

    def test_gold_tier_applies_fifteen_percent_discount(self):
        assert calculate_discount(100.0, "gold") == 85.0

    def test_unknown_tier_applies_no_discount(self):
        assert calculate_discount(100.0, "platinum") == 100.0

    def test_negative_price_raises_value_error(self):
        with pytest.raises(ValueError, match="Price cannot be negative"):
            calculate_discount(-50.0, "gold")

    def test_zero_price_returns_zero(self):
        assert calculate_discount(0.0, "gold") == 0.0

Notice what happened there. It caught the edge cases: unknown tier, negative price, zero price. It understood the discount logic and tested each branch. It even used proper pytest idioms with class organization and descriptive test names.

For JavaScript/TypeScript, it generates Jest tests with the same thoroughness:

typescriptCopy// Generated Jest tests for a React hook
import { renderHook, act } from '@testing-library/react';
import { useDebounce } from './useDebounce';

describe('useDebounce', () => {
  beforeEach(() => {
    jest.useFakeTimers();
  });

  afterEach(() => {
    jest.runOnlyPendingTimers();
    jest.useRealTimers();
  });

  it('returns initial value immediately', () => {
    const { result } = renderHook(() => useDebounce('initial', 500));
    expect(result.current).toBe('initial');
  });

  it('delays value update by specified milliseconds', () => {
    const { result, rerender } = renderHook(
      ({ value, delay }) => useDebounce(value, delay),
      { initialProps: { value: 'first', delay: 500 } }
    );

    rerender({ value: 'second', delay: 500 });
    expect(result.current).toBe('first');

    act(() => {
      jest.advanceTimersByTime(500);
    });

    expect(result.current).toBe('second');
  });
});

It handled the fake timers setup. It knew to use

for timer advancement. It tested the actual debounce behavior, not just that the hook exists.

The Debugger Agent: When Tests Fail

Here's where it gets interesting. Tests fail. That's the point - they're supposed to catch problems. But debugging failing tests can be its own rabbit hole.

The debugger agent follows a structured approach:

1. Capture error messages and stack traces
2. Identify reproduction steps
3. Isolate failure location
4. Implement minimal fix
5. Verify the solution

When a test fails, I invoke the debugger and it does something I never did consistently: it forms hypotheses and tests them methodically. It examines recent code changes, adds strategic debug logging, inspects variable states. It's doing the debugging process I knew I should do but often shortcut.

The output isn't just "here's a fix." It's:

• Root cause explanation
• Evidence supporting the diagnosis
• Specific code fix
• How to test the fix works
• How to prevent this in the future

That last part - prevention recommendations - is gold. It's turning individual bugs into systematic improvements.

TDD Actually Becomes Possible

The test-automator agent understands Test-Driven Development properly. Not just the concept, but the actual methodologies:

• Red-green-refactor cycle - Write failing test, make it pass, clean up
• Chicago School - State-based testing, verify outputs
• London School - Interaction-based, verify collaborations with mocks
• Property-based TDD - Generate properties that should always hold

I always understood TDD intellectually. Tests first, code second. But in practice, I'd write the code, promise myself I'd add tests, then... not.

Now I start with

on my function signature or interface. I get a suite of failing tests that define the behavior I'm implementing. Then I write code until they pass. The AI did the test-writing part that was blocking me.

It's TDD with training wheels - and honestly, I'm not ashamed. If the result is more tested code, who cares how I got there?

Framework Support That's Actually Comprehensive

The plugin handles the frameworks I actually use:

• Python: pytest with fixtures, parametrization, mocking
• JavaScript/TypeScript: Jest, React Testing Library
• Web automation: Playwright, Selenium
• API testing: REST Assured, Postman collections
• Performance: K6, JMeter patterns

And it integrates with CI/CD. It generates tests that work with GitHub Actions, GitLab CI, Jenkins - whatever pipeline you're running. Parallel execution, dynamic test selection, containerized environments.

This isn't a toy that generates

. It's production-grade test automation.

The Shift

Here's what actually changed in my workflow:

Before: Feature done -> consider writing tests -> decide it's probably fine -> ship it -> pray

After: Feature started -> generate test scaffolding -> implement until green -> ship with confidence

The difference isn't discipline. I didn't suddenly become a better person who values testing more. The difference is friction. The unit-testing plugin made test generation fast enough that it fits naturally into my workflow instead of feeling like a separate project.

The test-automator handles the boilerplate. The debugger helps when things break. The

command turns "I should write tests" into "I have tests."

The Other Side of Quality: Code Review

Testing catches bugs. But what about architectural issues? Security vulnerabilities? Code that works but is a maintenance nightmare waiting to happen?

That's where the comprehensive-review plugin comes in. It's the other half of my quality assurance workflow now.

Architecture Auditing That Sees the Big Picture

The architect-reviewer agent examines your codebase at the structural level. It's looking at things I used to only catch during painful refactors six months later:

• Design pattern adherence - Is your code following the patterns you think it is, or has it drifted into something weird?
• Dependency analysis - Are your modules properly decoupled, or is everything tangled together?
• Scalability concerns - Will this architecture handle 10x the load, or will it fall over?
• Technical debt identification - Where are the shortcuts that will cost you later?

I've had it flag circular dependencies I didn't even realize existed. It spotted a service that had become a god object - handling authentication, logging, database access, and email sending because we kept adding "just one more thing."

Security Review That Doesn't Sleep

The security-reviewer agent is paranoid in all the right ways. It scans for:

• Injection vulnerabilities - SQL, command, template injection patterns
• Authentication weaknesses - Hardcoded secrets, insecure token handling, broken session management
• Data exposure risks - Logging sensitive data, insecure API responses, PII leakage
• Dependency vulnerabilities - Known CVEs in your packages, outdated libraries with security patches

It caught a logging statement in our codebase that was dumping user passwords in debug mode. Technically it only ran in development. Technically. The kind of "technically" that ends up on HaveIBeenPwned.

The

Copy/review

Command

Just like

for testing, the command gives you instant, comprehensive analysis:

bashCopy/review path/to/your/code

It produces a structured report covering:

1. Critical issues - Things that need fixing now
2. Security concerns - Potential vulnerabilities ranked by severity
3. Architecture recommendations - Structural improvements
4. Code quality notes - Maintainability and readability suggestions
5. Performance observations - Bottlenecks and optimization opportunities

The output isn't just a list of problems. Each finding comes with:

• Why it matters
• How to fix it
• Code examples showing the improvement

Review Automation in CI/CD

The real power is automating this. Every PR can get an architecture and security review before a human even looks at it. The plugin integrates with GitHub Actions and other CI systems to run reviews on every push.

The agents act like tireless reviewers who never get bored, never miss obvious issues because they're distracted, and never feel awkward flagging problems in a senior developer's code.

Tests + Reviews = Confidence

Here's how the two plugins work together in my workflow:

1. Write feature code
2. Copy/test-generate
   creates the test suite
3. Run tests, fix failures with the debugger agent
4. Copy/review
   audits the implementation
5. Address architectural and security findings
6. Ship with actual confidence

Tests tell you the code works. Reviews tell you the code is good. Together, they catch the things that used to slip through to production and become 3am incidents.

Getting Started

Install both plugins from the agents-skills-plugins marketplace:

bashCopy/plugin install unit-testing@agents-skills-plugins
/plugin install comprehensive-review@agents-skills-plugins

Then try the unit-testing plugin on your most untested function:

bashCopy/test-generate path/to/your/file.py

Watch it analyze your code, identify edge cases, generate mocks, and produce a complete test suite. Then run those tests and see what breaks.

And run a review on code you're about to ship:

bashCopy/review path/to/your/feature

Get architecture feedback, security analysis, and code quality suggestions before that PR goes out.

For more tools that actually change how you work, check out chainbytes.com. And the full agents-skills-plugins collection at github.com/EricGrill/agents-skills-plugins.

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I spent years feeling guilty about not writing enough tests and not doing thorough code reviews. Turns out the problem wasn't motivation - it was tooling. The right tools don't make you want to test and review more. They make quality assurance feel like less of a thing you're forcing yourself to do.

Tests and reviews aren't chores when AI handles the tedious parts. They're just... how you ship code that actually works and doesn't wake you up at 3am.

"The best test is the one that exists. The best code review is the one that happens before production."