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Nw Hexagonal Testing

Organization
nWave-ai
nw-hexagonal-testing

5-layer agent output validation, I/O contract specification, vertical slice development, and test doubles policy with per-layer examples

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-hexagonal-testing
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Hexagonal Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-hexagonal-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-hexagonal-testing .claude/skills/nw-hexagonal-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Hexagonal Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Hexagonal Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Hexagonal Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hexagonal Testing and Output Validation

5-Layer Output Validation Framework

Validates agent OUTPUTS, not TDD testing methodology.

Layer 1: Unit Testing (Output Validation)

Validate individual software-crafter outputs.

yaml
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'"

Layer 2: Integration Testing (Handoff Validation)

Validate handoffs to next agent. Next agent must consume outputs without clarification.

  • Deliverables complete: all expected artifacts present
  • Validation status clear: quality gates passed/failed explicit
  • Context sufficient: next agent can proceed without re-elicitation

Layer 3: Adversarial Output Validation

Challenge output quality through adversarial scrutiny of generated code:

  • SQL injection vulnerabilities? | XSS vulnerabilities? | Null/undefined/empty input handling?
  • Integer overflow/underflow? | Graceful failure vs crash? | Exception handling appropriateness?

Pass criteria: all critical challenges addressed, edge cases documented and handled.

For peer review and escalation protocols, load the review-dimensions skill.

Input/Output Contract

Inputs

  • Required: user_request (non-empty command string) | context_files (existing readable file paths)
  • Optional: configuration (YAML/JSON) | previous_artifacts (outputs from prior wave for handoff)

Outputs

  • Primary: code artifacts (src/**/*, strictly necessary only) | documentation (docs/develop/, minimal essential)
  • Secondary: validation_results (gate pass/fail status) | handoff_package (deliverables, next_agent, validation_status)
  • Policy: any document beyond code/test files requires explicit user approval before creation

Side Effects

  • Allowed: file creation (src/, tests/ only) | file modification with audit trail | log entries
  • Forbidden: unsolicited documentation | deletion without approval | external API calls | credential access
  • Requires permission: documentation beyond code/test files | summary reports | analysis documents

Error Handling

  • Invalid input: validate first, clear error, do not proceed with partial inputs
  • Processing error: log context, return to safe state, actionable user message
  • Validation failure: report failed gates, withhold artifacts, suggest remediation

Vertical Slice Development

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.

Testing Boundaries per Architectural Layer

LayerTest StrategyAdapter SelectionRationale
DomainPure unit test, zero I/ON/A (no adapters)Domain is pure functions — test with pure inputs
ApplicationInMemory ports for focused scenariosInMemory doublesApplication orchestrates — test logic, not I/O
AdapterREAL 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 |

Key Insight

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.

Integration Surface Ratio Heuristic

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.

Strategy D Fixture Template

python
# 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.

Frequently asked questions

What does the Nw Hexagonal Testing AI skill do?

5-layer agent output validation, I/O contract specification, vertical slice development, and test doubles policy with per-layer examples

Why use Nw Hexagonal Testing on TypingMind?

Because you install it once and use it with any model. Nw Hexagonal Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Hexagonal Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-hexagonal-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Hexagonal Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Hexagonal Testing?

As many as you like. As long as a model supports skills, you can use Nw Hexagonal Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Hexagonal Testing AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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