Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nWave-ai/nWave --skill nw-hexagonal-testinggit clone --depth 1 https://github.com/nWave-ai/nWaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nwave-ai/nwave/nw-hexagonal-testing)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-hexagonal-testing"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-hexagonal-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-hexagonal-testing"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-hexagonal-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 65 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01175 |
| Opus 5.5 | $0.00012 | $0.00470 |
| Sonnet 5.5 | $0.00006 | $0.00235 |
| Haiku 4.5 | $0.00003 | $0.00118 |
Grade A, and why
nw-hexagonal-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 116 lines · 29 tokens per session scan A 826322f61303
nw-hexagonal-testing is a skill published in the GitHub repository nWave-ai/nWave (624 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,175 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
principle-test-behavior-not-implementation
Apply when you write, change, or keep a test. Assert what the code's users can observe, so that the test fails when that behavior breaks and keeps passing when only the internals change.
java-testing-junit-mockito
Use when writing tests for Java (Quarkus or Spring) code - JUnit Jupiter structure, Mockito for collaborators, test slices, Testcontainers, and framework-native integration tests, test-first.
test-writing
Writes meaningful tests that actually catch bugs.
rust-testing
Rust testing patterns including unit tests, integration tests, async testing, property-based testing, mocking, and coverage. Follows TDD methodology.
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
nemo-automodel-recipe-development
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.