neuron-evaluation

neuron-evaluation is a skill for Claude Code, Codex from neuron-core/neuron-ai. It costs 102 tokens per session (8,945 once invoked), scanned A, original, MIT.

A guide to evaluating AI systems with test data, checks, and result reports. Each evaluation runs the system on dataset items and compares its output with rules called assertions.

In plain words
What is it for?
Use it to create evaluators, load data from arrays or JSON files, validate outputs, and collect evaluation results.
Why use it?
It replaces informal spot checks with repeatable tests that show whether an AI system produces acceptable results across known examples.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create evaluators, load data from arrays or JSON files, validate outputs, and collect evaluation results.

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Install with agentmods
npx agentmods add skills/neuron-core/neuron-ai/neuron-evaluation
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 · 2,140 stars · on GitHub · neuron-ai.dev

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.

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

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for neuron-evaluation

README.md
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agentmods 80×15 button for neuron-evaluation

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Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,945 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00102 $0.08945
Opus 5.5 $0.00041 $0.03578
Sonnet 5.5 $0.00020 $0.01789
Haiku 4.5 $0.00010 $0.00894

Measured 8d ago against content hash 91b05085e34e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neuron-evaluation 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 8d 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.

skills/neuron-evaluation/SKILL.md · 1,169 lines

How it starts

The opening of the file, as written. The whole thing — 1,169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Neuron AI Evaluation

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\UniqueIdGenerator;

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
    {
        // A fresh thread per item, so no item sees another item's conversation
        $response = MyAgent::make()
            ->setThreadId(UniqueIdGenerator::generateId('eval_'))
            ->chat(new UserMessage($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 · 1,169 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. 8d ago Changed · -81 lines · -11 tokens per session 91b05085e34e
  2. 12d ago Changed a3333f363797
  3. 15d ago First seen · 1,250 lines · 113 tokens per session scan A 60bb9def8c11

Subscribe to this mod's changes

neuron-evaluation is a skill published in the GitHub repository neuron-core/neuron-ai (2,140 stars, last pushed yesterday), licensed MIT. It adds 102 tokens to every session and 8,945 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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