Tdd

Test-Driven Development

> Upstream: Adapted from mattpocock/skills/tdd. Vocabulary aligned with this repo's architecture-language/LANGUAGE.md. Used as the canonical TDD reference by /si and /si-quick.

Philosophy

Core principle: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.

Good tests are integration-style: they exercise real code paths through public APIs. They describe what the system does, not how it does it. A good test reads like a specification — "user can checkout with valid cart" tells you exactly what capability exists. These tests survive refactors because they don't care about internal structure.

Bad tests are coupled to implementation. They mock internal collaborators, test private methods, or verify through external means (like querying a database directly instead of using the interface). Warning sign: your test breaks when you refactor, but behavior hasn't changed. If renaming an internal function fails tests, those tests were testing implementation, not behavior.

See [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.

Named principles (from Software Engineering at Google):

• Beyoncé Rule — "if you liked it, you should have put a test on it." If a behavior matters, it has a test; anything untested is fair game to break and nobody will notice.
• Test pyramid (≈80/15/5) — favor many fast unit tests, fewer integration tests, very few end-to-end. An inverted pyramid (mostly E2E) is slow and flaky.
• DAMP over DRY in tests — tests may repeat themselves for readability. A test should be obvious in isolation; don't hide its meaning behind shared helpers the way you would in production code.

Anti-pattern: horizontal slices

DO NOT write all tests first, then all implementation. This is "horizontal slicing" — treating RED as "write all tests" and GREEN as "write all code."

This produces bad tests:

• Tests written in bulk test imagined behavior, not actual behavior
• You end up testing the shape of things (data structures, function signatures) rather than user-facing behavior
• Tests become insensitive to real changes — they pass when behavior breaks, fail when behavior is fine
• You outrun your headlights, committing to test structure before understanding the implementation

Correct approach: Vertical slices via tracer bullets. One test → one implementation → repeat. Each test responds to what you learned from the previous cycle. Because you just wrote the code, you know exactly what behavior matters and how to verify it.

WRONG (horizontal):
  RED:   test1, test2, test3, test4, test5
  GREEN: impl1, impl2, impl3, impl4, impl5

RIGHT (vertical):
  RED→GREEN: test1→impl1
  RED→GREEN: test2→impl2
  RED→GREEN: test3→impl3
  ...

Workflow

1. Planning

Before writing any code:

• [ ] Confirm with user what interface changes are needed
• [ ] Confirm with user which behaviors to test (prioritize)
• [ ] Identify opportunities for [deep modules](deep-modules.md) (small interface, deep implementation)
• [ ] Design interfaces for [testability](interface-design.md)
• [ ] List the behaviors to test (not implementation steps)
• [ ] Get user approval on the plan

Ask: "What should the public interface look like? Which behaviors are most important to test?"

You can't test everything. Confirm with the user exactly which behaviors matter most. Focus testing effort on critical paths and complex logic, not every possible edge case.

2. Tracer bullet

Write ONE test that confirms ONE thing about the system:

RED:   Write test for first behavior → test fails
GREEN: Write minimal code to pass → test passes

This is your tracer bullet — proves the path works end-to-end.

3. Incremental loop

For each remaining behavior:

RED:   Write next test → fails
GREEN: Minimal code to pass → passes

Rules:

• One test at a time
• Only enough code to pass current test
• Don't anticipate future tests
• Keep tests focused on observable behavior

4. Refactor

After all tests pass, look for [refactor candidates](refactoring.md):

• [ ] Extract duplication
• [ ] Deepen modules (move complexity behind simple interfaces)
• [ ] Apply SOLID principles where natural
• [ ] Consider what new code reveals about existing code
• [ ] Run tests after each refactor step

Never refactor while RED. Get to GREEN first.

Checklist per cycle

[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Code is minimal for this test
[ ] No speculative features added

Integration with this repo's flow

• /si and /si-quick defer to this skill for the canonical TDD discipline.
• developer-agent enforces vertical slicing during implementation.
• senior-architecture-reviewer verifies TDD compliance via git history (test commits precede implementation commits).

Common Rationalizations

The excuses that quietly turn TDD back into test-after:

| Rationalization | Reality | |---|---| | "I'll add tests after the code works" | That's test-after, not TDD. The test no longer drives the design and tends to encode whatever the code happens to do. RED comes before code. | | "Testing the implementation is fine, it's faster" | Tests bound to internals break on every refactor and stop being a safety net. Test behavior through the public interface. | | "This slice is small, I'll skip the failing-test step" | If you didn't watch it fail, you don't know the test tests anything. A test that never went RED can pass for the wrong reason. | | "I'll build the whole layer, then test the stack" | Horizontal slices defer integration risk to the end. Build thin vertical slices that prove the whole path early (see Anti-pattern above). | | "Tests are green-ish, I'll refactor now" | Never refactor while RED. Get fully GREEN first, then refactor with the net in place. |

Red Flags

• Code committed before any failing test for it exists.
• A test that has never been observed to fail.
• Tests that assert on private methods or internal state.
• A growing pile of untested code "to be covered later."
• Refactoring while the suite is red.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: alexandrbasis
• Source: alexandrbasis/claudops
• License: MIT

Install and usage instructions live in the source repository linked above.