A deprecated linter for Go source code that checks for style violations according to Go conventions.
Golint is a deprecated linter for Go source code that focuses on identifying style mistakes rather than correctness issues. It was designed to match the accepted coding style of the open-source Go project and was used internally at Google. The tool provides suggestions based on the conventions outlined in Effective Go and the CodeReviewComments wiki.
Go developers and teams seeking to align their code with the stylistic conventions of the official Go project, particularly those working on open-source Go codebases or within environments like Google's internal codebase.
Developers chose Golint for its specific focus on Go's stylistic conventions as documented in Effective Go and CodeReviewComments, offering editor integration with Vim and Emacs. It was distinct from gofmt (which reformats) and govet (which checks correctness), providing a non-enforcement philosophy that treated findings as suggestions rather than build-breaking errors.
[mirror] This is a linter for Go source code. (deprecated)
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Focuses exclusively on the stylistic conventions from Effective Go and CodeReviewComments, ensuring code matches the open-source Go project's standards as used at Google.
Outputs suggestions in Vim quickfix format with provided configurations for Vim and Emacs, making it easy to integrate into existing editor workflows.
Uses the same import path syntax as the Go command, supporting filenames, directories, packages, and wildcards like './...' for broad coverage.
Treats findings as suggestions only, acknowledging false positives and negatives, which prevents build breakage and allows for manual review.
The project is officially deprecated with no future updates or fixes, making it unsuitable for long-term use or new development.
Only checks for style mistakes, not correctness issues, requiring additional tools like go vet for comprehensive linting, which increases toolchain complexity.
The README admits it has both false positives and false negatives, reducing its trustworthiness for automated enforcement or critical code reviews.