dsh-agent-arena

When choosing an AI coding assistant, many developers face a common dilemma: different models boast various strengths in their marketing, but which one is truly best suited for their specific task and codebase? Blindly trying multiple models is time-consuming and makes objective comparison difficult. dsh-agent-arena addresses this by providing a controlled 'arena' where you can fairly pit multiple models against each other based on a real coding objective (like implementing a feature or fixing a bug).

Its core capability lies in creating a fully isolated and reproducible evaluation environment. The plugin runs each model's modifications in a separate Git worktree and then rigorously judges the results using your predefined, deterministic validation commands (such as unit tests or linting). The process is transparent and auditable; you can review the full code diffs from each model, and the system automatically selects a 'winner' based on validation results weighted by you. It also supports safe, one-click application of the winning changes.

This plugin is ideal for technical leaders, architects, and engineering teams focused on efficiency. If you are evaluating or selecting AI coding tools for your project, dsh-agent-arena helps you move beyond vendor hype and obtain evidence based on your own codebase and test criteria. It's also valuable for research communities to benchmark and compare model performance in real-world code generation scenarios.