github/git-sizer

Compute various size metrics for a Git repository, flagging those that might cause problems

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Summary Information

Updated 51 minutes ago
Type:CLI ToolCategory(s):Git & Version ControlDeveloper Tools
Added to GitGenius on September 17th, 2026
Created on January 28th, 2018
Open Issues & Pull Requests: 20 (+0)
GitHub issues: Enabled
Number of forks: 201
Total Stargazers: 4,076 (+0)
Total Subscribers: 44 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 42.0 hours
Mean response time: 65.2 days
90th percentile: 236.2 days
Tracked items: 6

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Issue Activity (beta)

Open issues: 7
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 419 days
Stale 30+ days: 6
Stale 90+ days: 6

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

git-sizer is a command-line tool that computes size metrics for Git repositories and identifies measurements that might cause operational problems.

The tool addresses the challenge of understanding what makes a Git repository large or problematic. It analyzes repositories across multiple dimensions—including object counts, blob sizes, tree depths, and reference counts—and flags metrics that exceed thresholds known to cause performance issues or compatibility problems with Git hosting platforms and clients. This approach helps developers diagnose repository health without needing to manually inspect the underlying Git objects.

The tool suits developers and repository maintainers who need to understand repository structure and identify potential scaling issues before they impact workflows. It is particularly valuable for teams managing large monorepos or repositories that have accumulated significant history. The README does not compare git-sizer to alternative tools, so no comparative guidance can be offered.

The project shows consistent maintenance with regular updates addressing both bug fixes and feature improvements. Development activity demonstrates responsiveness to reported issues and a willingness to refine the tool's analysis capabilities based on user feedback. The codebase remains actively refined, with attention paid to improving the accuracy and usefulness of the metrics reported.