Trainlint

Trainlint is an offline checker and agent skills for machine learning bugs that do not crash.

Trainlint is a small offline checker and a collection of agent skills designed to catch silent machine learning bugs that do not crash your code, such as data leakage, incorrect training modes, and loss or metric mistakes. It parses Python source code using the standard library ast module without running the code, touching the network, or requiring a GPU. It includes fourteen specific rules covering issues like scalers fitted before splits, missing random seeds, and swapped evaluation arguments, along with four agent skills for coding assistants. The tool outputs text or JSON results and supports per-line ignore comments for flexibility. It is built for machine learning developers and coding agents who want to catch silent training and evaluation logic errors early.

Trainlint at a glance

Best for: Software engineer, Researcher
Runs on: CLI, open source
Pricing: Free
Released: November 2026
Category: AI coding tool
Website: github.com/satvik-jain/trainlint

Key features

Detects fourteen rules for silent machine learning bugs
Parses Python source code with the ast module without execution
Provides four agent skills for coding assistants
Supports text and JSON output formats
Allows per-line ignore comments to silence findings
Runs completely offline with no network or GPU required

Use it for

Checking machine learning training scripts for data leakage
Finding incorrect model evaluation modes and loss calculations
Integrating error checking into GitHub Actions workflows
Equipping coding agents with skills to avoid machine learning bugs

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