neuron-evaluation-engineer

neuron-evaluation-engineer is a skill for Claude Code, Codex from neuron-core/neuron-ai. It costs 77 tokens per session (4,355 once invoked), scanned A, original, MIT.

Instructions for evaluating AI systems in Neuron AI. An evaluation runs an AI system against dataset items, checks the results with assertions, and reports what happened.

In plain words
What is it for?
Use them to create evaluators, prepare datasets, define output checks, run AI tests, and review evaluation results.
Why use it?
They provide a repeatable way to test whether an AI system produces acceptable results across many examples.

Skill for Claude CodeCodex

About the project

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.

neuron-core/neuron-ai · 2,088 stars · on GitHub

Install

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.

agentmods
npx agentmods add skills/neuron-core/neuron-ai/neuron-evaluation-engineer
Any agent
npx skills add neuron-core/neuron-ai --skill neuron-evaluation-engineer
Clone the repo
git clone --depth 1 https://github.com/neuron-core/neuron-ai

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for neuron-evaluation-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/neuron-core/neuron-ai/neuron-evaluation-engineer.svg)](https://agentmods.dev/skills/neuron-core/neuron-ai/neuron-evaluation-engineer)
Your own site
<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>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,355 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash 546a30c710f8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/neuron-evaluation-engineer/SKILL.md · 735 lines

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:

  1. Evaluators - Test classes that define what to run and how to validate
  2. Datasets - Test data sources (arrays, JSON files)
  3. Assertions - Validation rules for checking outputs
Dataset Items → Evaluator::run() → Output → Evaluator::evaluate() → Assertions → Results

Evaluation Flow

For each dataset item:

  1. setUp() - Initialize resources (once per evaluator)
  2. run(datasetItem) - Execute your AI logic
  3. evaluate(output, datasetItem) - Assert against expected results
  4. 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
        );
    }
}

Read the full file on GitHub · 735 lines

Changes

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.

  1. 5d ago First seen · 735 lines · 77 tokens per session scan A 546a30c710f8

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

neuron-structured-output

Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…

neuron-core/neuron-laravel · 104 tokens

neuron-rag-specialist

Implement RAG (Retrieval-Augmented Generation) with Neuron AI including vector stores, embeddings providers, document loaders, and retrieval strategies. Use this skill whenever the user mentions RAG, retrieval, vector search, document retrieval, semantic search, knowledge bases, chat with documents, or wants to build…

neuron-core/neuron-laravel · 95 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

azure-openai-to-responses

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API…

microsoft/ai-agents-for-beginners · 185 tokens

flow-nexus-neural

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus.

ruvnet/ruflo · 21 tokens

agent-v3-memory-specialist

Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.

ruvnet/ruflo · 25 tokens