goodwithtech/dockle

Container Image Linter for Security, Helping build the Best-Practice Docker Image, Easy to start

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goodwithtech/dockle has 3k+ stars and 150+ forks on GitHub, is written in Go, and was last pushed on Aug 10, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 50 minutes ago
Added to GitGenius on September 21st, 2026
Created on May 23rd, 2019
Open Issues & Pull Requests: 53 (+0)
GitHub issues: Enabled
Number of forks: 171
Total Stargazers: 3,300 (+0)
Total Subscribers: 23 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 172.8 days
Mean response time: 404.7 days
90th percentile: 709.0 days
Tracked items: 12

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Issue Activity (beta)

Open issues: 13
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 713 days
Stale 30+ days: 13
Stale 90+ days: 12

Recent activity

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

Top labels

  • bug (7)
  • enhancement (4)
  • help wanted (1)
  • question (1)

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Detailed Description

Dockle is a container image linter that analyzes Docker images for security issues and best-practice violations.

The tool addresses the problem of insecure or poorly-constructed container images by scanning built images against a set of predefined rules. It works by inspecting the layers and metadata of Docker images to identify configuration problems, missing security controls, and deviations from container best practices. Rather than requiring changes to build processes, Dockle operates on already-built images, making it straightforward to integrate into existing workflows.

Dockle suits teams that want to enforce container security standards without significant refactoring. It is particularly useful for organizations building container images regularly and needing automated checks before deployment. The tool is designed to be easy to start with, requiring minimal configuration to begin linting images. It works well in Kubernetes environments and continuous integration pipelines where image validation is a prerequisite step.

Development activity shows consistent engagement with the codebase. The project receives regular updates addressing issues and adding new checks. Pull requests are reviewed and merged at a steady pace, indicating active maintenance. The maintainers respond to reported issues and incorporate community feedback into the tool's rule set.