# Data Modeling

> Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a…

- **Type:** Skill
- **Install:** `agentstack add skill-agentscope-ai-qwenpaw-data-data-modeling`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [agentscope-ai](https://agentstack.voostack.com/s/agentscope-ai)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [agentscope-ai](https://github.com/agentscope-ai)
- **Source:** https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/datapaw-skills/skills/workflows/data-modeling

## Install

```sh
agentstack add skill-agentscope-ai-qwenpaw-data-data-modeling
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data Modeling

When users present a data modeling request, start from data understanding and complete the full pipeline—from exploration to output. Run fully automated end-to-end; never pause to ask the user for intermediate decisions.

## Core Principles

- Strictly adhere to task requirements regarding features, preprocessing methods (if specified), and output format.
- All preprocessing parameters must be **fitted on the training set** and **consistently transformed on the test set**.
- Prioritize efficient methods and models from lightweight libraries like scikit-learn to quickly validate feasibility.
- Always consider data scale: for large sample sizes or high-dimensional features, implement OOM safeguards (e.g., sampling, streaming processing, avoiding full One-Hot encoding, limiting tree depth, etc.).
- Ensure outputs are well-structured and logically reproducible; avoid data leakage or dimension mismatches.
- 🚫 **No plotting allowed**: You cannot view charts. All feature analysis must be performed through **computed statistics only**.

## Data Exploration

Perform rapid preliminary analysis on the provided data:

- Check shape: number of samples and features.
- Identify feature types:
  - Numerical (continuous/discrete)
  - Categorical (nominal/ordinal)
  - Temporal (datetime)
  - Special fields: text, IDs, high-cardinality features, etc.

## Implementation Workflow

**Modeling Strategy: Start simple, iterate progressively.**

### ✅ Phase 1: Quick Baseline (Mandatory)

Use **simple, efficient methods and models** to establish an end-to-end pipeline and **generate an initial prediction**:

- **Essential Preprocessing**:
  - Handle missing values: impute with reasonable defaults (e.g., mean, median, mode) based on feature type.
  - Drop irrelevant columns: e.g., IDs, UUIDs, serial numbers, or other unique identifiers.
  - Encoding:
    - Numerical features: standardize if needed.
    - Categorical features:
      - **High-cardinality categories** (unique values ≥ 10): Avoid One-Hot encoding to prevent dimension explosion. Use compact representations such as Label Encoding, Frequency Encoding, Target Encoding, or Hash Encoding. **Choose the encoding method based on data characteristics and model compatibility.**
      - **Low-cardinality categories** (unique values  ⚠️ Always balance performance gains against implementation complexity: **Do not incur 10× maintenance cost for a 1% improvement.**

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [agentscope-ai](https://github.com/agentscope-ai)
- **Source:** [agentscope-ai/QwenPaw-Data](https://github.com/agentscope-ai/QwenPaw-Data)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-agentscope-ai-qwenpaw-data-data-modeling
- Seller: https://agentstack.voostack.com/s/agentscope-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.