Neuron AI is a PHP framework for building AI applications in which agents connect language models, tools, data loaders, vector databases, memory, and user interfaces. PHP developers use it to create and manage applications with agent workflows, multi-agent coordination, streaming, monitoring, and human involvement.
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 agentmods add skills/neuron-core/neuron-ai/neuron-evaluation-engineernpx skills add neuron-core/neuron-ai --skill neuron-evaluation-engineergit clone --depth 1 https://github.com/neuron-core/neuron-aiWrote 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-ai/neuron-evaluation-engineer)<a href="https://agentmods.dev/skills/neuron-core/neuron-ai/neuron-evaluation-engineer"><img src="https://agentmods.dev/badge/skills/neuron-core/neuron-ai/neuron-evaluation-engineer.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.04355 |
| Opus 5 | $0.00039 | $0.02178 |
| Sonnet 5 | $0.00015 | $0.00871 |
| Haiku 4.5 | $0.00008 | $0.00436 |
Grade A, and why
neuron-evaluation-engineer 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- neuron-evaluation-engineer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 735 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuron AI Evaluation Engineer
This skill helps you create and run evaluations for AI systems in Neuron AI. The evaluation system provides dataset-driven testing with flexible assertions, comprehensive result reporting, and extensible output drivers.
Core Concepts
The Evaluation System
Evaluations test AI systems using three main components:
- Evaluators - Test classes that define what to run and how to validate
- Datasets - Test data sources (arrays, JSON files)
- Assertions - Validation rules for checking outputs
Dataset Items → Evaluator::run() → Output → Evaluator::evaluate() → Assertions → Results
Evaluation Flow
For each dataset item:
setUp()- Initialize resources (once per evaluator)run(datasetItem)- Execute your AI logicevaluate(output, datasetItem)- Assert against expected results- Repeat for next item
Note: Each evaluation starts with a fresh assertion executor - no manual reset needed.
Creating Custom Evaluators
Basic Evaluator
use NeuronAI\Evaluation\BaseEvaluator;
use NeuronAI\Evaluation\Contracts\DatasetInterface;
use NeuronAI\Evaluation\Assertions\StringContains;
use NeuronAI\Evaluation\Dataset\ArrayDataset;
use NeuronAI\Agent;
use NeuronAI\Agent\SystemPrompt;
class ContainsEvaluator extends BaseEvaluator
{
public function getDataset(): DatasetInterface
{
return new ArrayDataset([
[
'text' => 'I love this product!',
'content' => 'product',
],
[
'text' => 'This is terrible.',
'content' => 'positive',
],
]);
}
public function run(array $datasetItem): mixed
{
$response = MyAgent::make()->chat(
new UserMessage($datasetItem['text'])
)->getMessage();
return $response->getContent();
}
public function evaluate(mixed $output, array $datasetItem): void
{
$this->assert(
new StringContains($datasetItem['content']),
$output
);
}
}
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.
- 5d ago First seen · 735 lines · 77 tokens per session scan A 546a30c710f8
neuron-evaluation-engineer is a skill published in the GitHub repository neuron-core/neuron-ai (2,088 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 4,355 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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