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Vinkius

simple-statistics Connector for AI agents.

1 live capability

Normalize your machine learning datasets with exact mathematical precision.

Live agent request simple-statistics / Connector

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AI Agent

Why people use simple-statistics

Feature Scaler Engine: Fix ML Data Hallucinations

With this Connector, you just tell your agent to scale the columns. The agent calls the capability, the math happens on your hardware, and you get the result in seconds. You skip the notebook setup and the copy-pasting, getting a perfect normalization every time.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

It replaces unreliable AI math with deterministic local processing for your datasets.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Neural Network Training

    A data scientist needs to prep 5,000 rows of user data.

  2. Real-world use case 02

    K-Means Clustering

    An analyst has features with different units like weight and height.

  3. Real-world use case 03

    Image Processing

    Someone needs to normalize PixelIntensity values.

Complete set · 1capability

The complete simple-statistics capability set.

These are the exact actions your AI can choose when you ask it to work with simple-statistics.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through simple-statistics.

  1. 01 Capability

    Scale features

    Performs Z-Score or MinMax scaling on numeric columns. It returns the transformed data along with the exact means, standard deviations, and bounds.

Set up in minutes

One URL. Then ask simple-statistics to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use simple-statistics from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_F7GwWGq3LmnuDkiU1owmlZfI1wLX3GKZ5metNm37/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it simple-statistics, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable simple-statistics for the conversation.

Where the request belongs

Work simple-statistics can move forward.

Built around the request

This is for the data scientist or ML engineer who needs high-precision data prep without the risk of AI math errors.

01

Data Scientist

Cleaning up messy datasets for K-Means or neural nets before training.

02

ML Engineer

Automating feature engineering pipelines without worrying about hallucination.

03

Data Analyst

Quickly normalizing variables for comparative analysis across different units.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Outlier Detection Engine logo
01 1 capability

Outlier Detection Engine

Identify statistical anomalies in massive datasets local using deterministic Z-Score and IQR methods. Stop LLMs from guessing which rows are outliers.

View Connector
PCA Dimensionality Engine logo
02 1 capability

PCA Dimensionality Engine

Perform native Principal Component Analysis to safely reduce high-dimensional datasets without losing critical variance.

View Connector
Missing Value Imputer logo
03 1 capability

Missing Value Imputer

Automatically fill NaN and missing values in datasets using Mean, Median, Mode, or Zero strategies deterministically local. Essential ML data preparation.

View Connector
One-Hot Encoder Engine logo
04 1 capability

One-Hot Encoder Engine

Deterministically convert categorical text columns into dummy binary variables local. Essential machine learning data prep without LLM data corruption.

View Connector
SMOTE Oversampling Engine logo
05 1 capability

SMOTE Oversampling Engine

Balance skewed datasets instantly by generating mathematically sound synthetic minority data points via KNN.

View Connector
Statistics Engine logo
06 5 capabilities

Statistics Engine

A zero-latency statistical engine to process datasets. Instantly compute the exact mean, median, mode, standard deviation, and percentiles completely local.

View Connector

Bring your own AI

Change the model, client or framework. Keep simple-statistics connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
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  • Roo Code
  • Zencoder
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  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about simple-statistics.

The practical details behind the request, access and result.

Can the Feature Scaler Engine handle large datasets?

Yes, it can handle thousands of rows easily because it performs the math on your local CPU rather than asking the AI to do the arithmetic.

Does this Connector keep my data private?

Yes, all scaling and normalization happens locally on your machine. Your sensitive training data is never sent to an external service.

What is the difference between Z-Score and MinMax scaling?

Z-Score centers your data around a mean of 0 with a standard deviation of 1. MinMax scales your data into a specific range, like 0 to 1. This Connector lets you choose either method.

Can I scale multiple columns at once with Feature Scaler Engine?

Yes, you can specify multiple columns in a single request. This makes it much faster to prepare your entire dataset for machine learning.

Does this work for text or categorical data?

No, this Connector is specifically designed for numeric columns. It is built to handle the mathematical precision required for feature engineering.

Will this help prevent my AI from making math mistakes?

Exactly. By offloading the calculations to this Connector, you ensure that your agent uses perfect math instead of guessing the averages or ranges.

What is the difference between Standard and MinMax scaling?

Standard scaling (Z-Score) centers data at 0 with a variance of 1, ideal for algorithms that assume normally distributed features. MinMax compresses all values precisely between 0 and 1, ideal for neural networks and distance-based algorithms.

Are the computed scaling parameters returned for inverse transforms?

Yes. The JSON response includes the exact Mean and Std Dev (for Standard) or Min and Max (for MinMax) used to scale each column, enabling precise inverse transformations when needed.

Can it scale 10+ columns at once?

Absolutely. Pass a JSON array of all column names and they will all be scaled simultaneously in memory. The engine processes each column independently with its own computed metrics.

One connection away

Give your agent a direct line to simple-statistics.

Connect simple-statistics once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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