hiyohiyo/crystaldiskinfo

CrystalDiskInfo

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Summary Information

Updated 7 minutes ago
Added to GitGenius on September 20th, 2026
Created on April 16th, 2016
Open Issues & Pull Requests: 128 (+0)
GitHub issues: Enabled
Number of forks: 277
Total Stargazers: 3,348 (+0)
Total Subscribers: 52 (+0)

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Issue Activity (beta)

Open issues: 37
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 494 days
Stale 30+ days: 35
Stale 90+ days: 32

Recent activity

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

Top labels

  • enhancement (1)

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Detailed Description

CrystalDiskInfo is a disk health monitoring tool that reads and displays S.M.A.R.T. data from hard drives and solid-state drives.

The tool solves the problem of understanding drive health by parsing S.M.A.R.T. attributes from storage devices and presenting them in an accessible format. It works by querying ATA and NVMe drive firmware to retrieve health indicators, then interpreting those raw values to give users a clear picture of whether their storage is operating normally or showing signs of failure. This approach allows users to catch drive degradation before data loss occurs.

CrystalDiskInfo suits anyone who needs to monitor storage reliability on Windows systems, from individual users concerned about their personal machines to system administrators managing multiple drives. It is particularly valuable for users with older drives or those running drives under heavy workloads, where early warning of failure becomes critical. The tool works with both traditional hard disk drives and modern NVMe solid-state drives, making it broadly applicable across different storage types.

Development activity shows consistent maintenance with regular updates addressing compatibility and functionality improvements. The project receives ongoing bug fixes and enhancements to support new drive models and firmware variations. Updates are released to handle emerging storage technologies and maintain compatibility with evolving Windows environments. The codebase demonstrates active refinement of the S.M.A.R.T. interpretation logic to improve accuracy of health assessments.