python-eval

Evaluate AI-generated Python code pipelines across correctness, formatting, performance, and reliability.

2|Updated Apr 20, 2026

Search Tags:#code quality #analytics #python #benchmarking #evaluation #scorecard

Ready to run

npx skills add https://github.com/sanjogkadayat-web/eval-framework --skill python-eval

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-eval
Source: https://github.com/sanjogkadayat-web/eval-framework/tree/main/python-eval
Command: npx skills add https://github.com/sanjogkadayat-web/eval-framework --skill python-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tiktoken, sqlfluff, flake8, pytest, pandas, pyyaml, duckdb, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

This skill solves the challenge of objectively evaluating AI-generated Python code by providing a reproducible, deterministic scorecard that measures performance, correctness, and reliability without manual intervention.

Core Features & Use Cases

Quantitative Benchmarking: Scores models across four dimensions (correctness, formatting, performance, and reliability) using pre-recorded metrics.
Structured Critic Review: Generates qualitative feedback using a specialized persona to identify strengths, failures, and mitigation strategies.
Use Case: Analytics teams can use this to compare Claude, ChatGPT, and Gemini on 30 realistic engineering tasks to determine which model is best suited for production ETL pipelines.

Quick Start

Run the python evaluation skill to generate a full comparative scorecard for all tasks.

Frequently Asked Questions about python-eval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I benchmark AI-generated Python code pipelines for production ETL tasks?▼

To benchmark AI-generated Python code pipelines, you can use a deterministic evaluation skill that calculates quantitative metrics across correctness, formatting, performance, and reliability dimensions using pre-recorded data. This produces a reproducible scorecard without manual intervention.

What metrics are used to evaluate the correctness and reliability of AI Python code?▼

Metrics for evaluating AI Python code correctness and reliability are calculated quantitatively using pytest for test validation, flake8 for formatting compliance, and performance tracking. These combine into a structured decision-ready scorecard.

Can I compare different AI models on analytics engineering tasks like schema validation and feature engineering?▼

Yes, you can compare different AI models on analytics engineering tasks. The evaluation applies deterministic scoring to ETL tasks, schema validation, and feature engineering, generating a comparative scorecard to determine which model is best suited for production.

Do I need pre-recorded evaluation data to run a Python benchmarking scorecard?▼

Yes, pre-recorded evaluation data is required to run the Python benchmarking scorecard. The deterministic scoring mechanism relies on these pre-existing metrics to evaluate AI-generated code and produce a structured critic review.

What is the best way to generate qualitative feedback for AI-generated Python scripts?▼

The best way to generate qualitative feedback for AI-generated Python scripts is by applying a specialized persona-based critic review. This identifies strengths, failures, and mitigation strategies alongside quantitative metrics to form a complete evaluation.