Measure pull request size metrics using git diff and log analysis.
npx skills add https://github.com/FScoward/senju --skill pr-size-guard
Please help me install this Agent Skill. Skill: pr-size-guard Source: https://github.com/FScoward/senju/tree/main/skills/pr-size-guard Command: npx skills add https://github.com/FScoward/senju --skill pr-size-guard
Developers often submit large pull requests that are difficult to review, leading to reduced code quality and slower feedback cycles.
Execute the measurement script to evaluate your current branch against the main branch and assess if the PR size is suitable for review.
High-intent search queries and answers about installing and using this skill.
You can enforce pull request size limits by running a script that measures code change metrics against predefined thresholds. This highlights potential review burdens, allowing developers to split large PRs for better code review quality.
Pull request size is measured using git diff and log analysis to evaluate line counts, file diversity, and impact zones. These metrics help identify if a code change exceeds manageable limits before pushing.
To check if your branch changes are too large, execute the measurement script to evaluate your current branch against the main branch. It assesses line count, file count, and affected layers to determine if the PR size is suitable for review.
Yes, the code review size monitor works without external dependencies. It relies solely on standard git diff and log analysis to measure code change sizes and alert users if changes exceed predefined thresholds.
The best way to automatically alert developers about large pull requests is by applying size threshold detection during branch preparation. It alerts users if changed code exceeds predefined limits, facilitating timely splits or justifications.
You should split a large pull request when the changed code exceeds predefined thresholds for line count and file diversity. The script highlights these potential review burdens to promote better review practices and faster feedback cycles.
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