5-layer agent output validation, I/O contract specification, vertical slice development, and test doubles policy with per-layer examples
npx skills add https://github.com/nWave-ai/nWave --skill nw-hexagonal-testing
Validates agent OUTPUTS, not TDD testing methodology.
Validate individual software-crafter outputs.
structural_checks:
- required_elements_present: true
- format_compliance: true
- quality_standards_met: true
quality_checks:
- completeness: "All required components present"
- clarity: "Unambiguous and understandable"
- testability: "Can be validated"
test_data_quality:
real_data: "Use real API responses as golden masters"
edge_cases: "Test null, empty, malformed, boundary conditions"
assertions: "Assert expected counts, not just 'any results'"
Validate handoffs to next agent. Next agent must consume outputs without clarification.
Challenge output quality through adversarial scrutiny of generated code:
Pass criteria: all critical challenges addressed, edge cases documented and handled.
For peer review and escalation protocols, load the review-dimensions skill.
Complete business capability per slice: UI -> Application -> Domain -> Infrastructure for a specific feature. Slices developed and deployed independently. Focus on business capability over technical layer.
For test doubles policy and violation examples, load the tdd-methodology skill.
| Layer | Test Strategy | Adapter Selection | Rationale |
|-------|--------------|------------------|-----------|
| Domain | Pure unit test, zero I/O | N/A (no adapters) | Domain is pure functions — test with pure inputs |
| Application | InMemory ports for focused scenarios | InMemory doubles | Application orchestrates — test logic, not I/O |
| Adapter | REAL I/O ALWAYS* | Real system (tmp_path, subprocess, DB) | Adapter IS the I/O boundary — testing with InMemory defeats the purpose |
*Exception: costly subprocesses (claude -p, LLM) and paid external APIs use contract smoke tests tagged @requires_external instead of real I/O. See nw-tdd-methodology Mandate 6 for the full adapter type → test type table.
| WS/E2E | Per declared strategy (A/B/C/D) | Real for local, fake for costly | WS proves wiring — InMemory proves nothing about wiring |
A pure function stub at a driven port boundary models the port's CONTRACT, not the external system's BEHAVIOR. For every driven port stub, verify that an adapter integration test with real I/O covers the behavioral gap.
If the system's primary job is coordinating external processes (orchestrators, ETL, deployment scripts), invest MORE in WS and adapter integration tests than unit tests. If the system's primary job is domain computation (pricing, validation, parsing), the traditional pyramid applies.
# conftest.py
import os
import pytest
@pytest.fixture
def subprocess_runner():
"""Returns real or fake subprocess runner based on E2E mode."""
if os.getenv("NWAVE_E2E_REAL_SUBPROCESS") == "1":
from myapp.adapters.real_subprocess_runner import RealSubprocessRunner
return RealSubprocessRunner()
from tests.doubles.fake_subprocess_runner import FakeSubprocessRunner
return FakeSubprocessRunner(exit_code=0, stdout="OK")
Naming convention for multi-port Strategy D: NWAVE_E2E_REAL_{PORT_NAME} per driven port (e.g., NWAVE_E2E_REAL_SUBPROCESS, NWAVE_E2E_REAL_DATABASE). Use NWAVE_E2E_MODE=real as composite flag to enable ALL real adapters at once for full E2E.
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 nwave-ai/nw-hexagonal-testing 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.