TDD: enforce RED-GREEN-REFACTOR, tests before code.
npx skills add https://github.com/HezaoHezao/poirot --skill test-driven-development
Write the test first. Watch it fail. Write minimal code to pass.
Core principle: If you didn't watch the test fail, you don't know if it tests the right thing.
Always:
Exceptions (ask the user first):
Thinking "skip TDD just this once"? Stop. That's rationalization.
NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST
Write code before the test? Delete it. Start over.
No exceptions:
Implement fresh from tests. Period.
Write one minimal test showing what should happen.
Good test:
def test_retries_failed_operations_3_times():
attempts = 0
def operation():
nonlocal attempts
attempts += 1
if attempts < 3:
raise Exception('fail')
return 'success'
result = retry_operation(operation)
assert result == 'success'
assert attempts == 3
Clear name, tests real behavior, one thing.
Bad test:
def test_retry_works():
mock = MagicMock()
mock.side_effect = [Exception(), Exception(), 'success']
result = retry_operation(mock)
assert result == 'success' # What about retry count? Timing?
Vague name, tests mock not real code.
Requirements:
MANDATORY. Never skip.
pytest tests/test_feature.py::test_specific_behavior -v
Confirm:
Test passes immediately? You're testing existing behavior. Fix the test.
Test errors? Fix the error, re-run until it fails correctly.
Write the simplest code to pass the test. Nothing more.
Good:
def add(a, b):
return a + b # Nothing extra
Bad:
def add(a, b):
result = a + b
logging.info(f"Adding {a} + {b} = {result}") # Extra!
return result
Don't add features, refactor other code, or "improve" beyond the test.
Cheating is OK in GREEN:
We'll fix it in REFACTOR.
MANDATORY.
# Run the specific test
pytest tests/test_feature.py::test_specific_behavior -v
# Then run ALL tests to check for regressions
pytest tests/ -q
Confirm:
Test fails? Fix the code, not the test.
Other tests fail? Fix regressions now.
After green only:
Keep tests green throughout. Don't add behavior.
If tests fail during refactor: Undo immediately. Take smaller steps.
Next failing test for next behavior. One cycle at a time.
Do not write all tests first and then all implementation. That is horizontal
slicing. Use vertical tracer bullets instead:
WRONG:
RED: test1, test2, test3, test4
GREEN: impl1, impl2, impl3, impl4
RIGHT:
RED→GREEN: test1→impl1
RED→GREEN: test2→impl2
RED→GREEN: test3→impl3
A tracer bullet is one end-to-end behavior slice. It proves the path works,
teaches you about the interface, and keeps each next test grounded.
"I'll write tests after to verify it works"
Tests written after code pass immediately. Passing immediately proves nothing:
Test-first forces you to see the test fail, proving it actually tests something.
"Deleting X hours of work is wasteful"
Sunk cost fallacy. The time is already gone. Your choice now:
| Excuse | Reality |
|--------|---------|
| "Too simple to test" | Simple code breaks. Test takes 30 seconds. |
| "I'll test after" | Tests passing immediately prove nothing. |
| "Already manually tested" | Ad-hoc ≠ systematic. No record, can't re-run. |
| "Deleting X hours is wasteful" | Sunk cost fallacy. Keeping unverified code is debt. |
| "Keep as reference, write tests first" | You'll adapt it. That's testing after. Delete means delete. |
| "Need to explore first" | Fine. Throw away exploration, start with TDD. |
| "TDD will slow me down" | TDD faster than debugging. Pragmatic = test-first. |
If you catch yourself doing any of these, delete the code and restart with TDD:
All of these mean: Delete code. Start over with TDD.
Before marking work complete:
Can't check all boxes? You skipped TDD. Start over.
| Problem | Solution |
|---------|----------|
| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |
| Test too complicated | Design too complicated. Simplify the interface. |
| Must mock everything | Code too coupled. Use dependency injection. |
| Test setup huge | Extract helpers. Still complex? Simplify the design. |
interactions, not replace the system under test
shouldn't break them
Production code → test exists and failed first
Otherwise → not TDD
No exceptions without the user's explicit permission.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take hezaohezao/test-driven-development from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.