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 neuron-core/neuron-laravel --skill neuron-testgit clone --depth 1 https://github.com/neuron-core/neuron-laravelWrote 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/neuron-core/neuron-laravel/neuron-test)<a href="https://agentmods.dev/skills/neuron-core/neuron-laravel/neuron-test"><img src="https://agentmods.dev/badge/skills/neuron-core/neuron-laravel/neuron-test/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/neuron-core/neuron-laravel/neuron-test"><img src="https://agentmods.dev/badge/skills/neuron-core/neuron-laravel/neuron-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00092 | $0.05568 |
| Opus 5.5 | $0.00037 | $0.02227 |
| Sonnet 5.5 | $0.00018 | $0.01114 |
| Haiku 4.5 | $0.00009 | $0.00557 |
Grade A, and why
neuron-test 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 15d 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 — 800 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuron AI Test
This skill helps you write comprehensive tests for Neuron AI applications using the built-in testing utilities in NeuronAI\Testing.
Testing Philosophy
Neuron AI provides fake implementations that:
- Never make real API calls - All AI provider calls are mocked
- Record all interactions - Inspect what was sent and when
- Provide fluent assertions - PHPUnit-style assertions for verification
Core Testing Utilities
1. FakeAIProvider
The primary tool for testing agents without real AI API calls.
use NeuronAI\Testing\FakeAIProvider;
use NeuronAI\Chat\Messages\AssistantMessage;
use NeuronAI\Chat\Messages\UserMessage;
// Create with predetermined responses
$provider = new FakeAIProvider(
new AssistantMessage('Hello! How can I help you?'),
new AssistantMessage('The answer is 42.')
);
// Or use static constructor
$provider = FakeAIProvider::make(
new AssistantMessage('Response 1'),
new AssistantMessage('Response 2')
);
Key Features:
- Responses are returned sequentially from queue
- Supports
chat(),stream(), andstructured()methods stream()derives its chunks from the queued message like a real provider:ReasoningChunkfor reasoning blocks,TextChunkfor text, then aToolArgumentChunksequence with each tool call's JSON inputs- Records all requests for assertion
2. FakeVectorStore
For testing RAG systems without real vector databases.
use NeuronAI\Testing\FakeVectorStore;
use NeuronAI\RAG\Document;
// Pre-populate with search results
$vectorStore = new FakeVectorStore([
new Document('France is a country in Europe. Its capital is Paris.'),
new Document('Germany is a country in Europe. Its capital is Berlin.'),
]);
// Or create empty and set results later
$vectorStore = FakeVectorStore::make();
$vectorStore->setSearchResults([
new Document('Relevant document content')
]);
Preset results are returned as they are, trimmed to the request's topK. Documents stored through addDocument() and addDocuments() must carry an embedding, exactly like a real store: let the RAG or a FakeEmbeddingsProvider embed them first.
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.
- 15d ago First seen · 800 lines · 92 tokens per session scan A 182fa1431cff
neuron-test is a skill published in the GitHub repository neuron-core/neuron-laravel (122 stars, last pushed 16d ago), licensed MIT. It adds 92 tokens to every session and 5,568 once invoked, about $0.0004 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 2026-10-01.
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