A text file analysis tool that detects non-inclusive language in source code and suggests inclusive alternatives.
woke is a text file analysis tool that scans source code to identify non-inclusive language and suggests more inclusive alternatives. It helps developers and teams create more welcoming and inclusive software by automatically detecting problematic terms like "master/slave" or "whitelist/blacklist." The tool supports integration into development workflows to promote long-term commitment to inclusive language practices.
Software development teams, open-source maintainers, and organizations aiming to improve inclusivity in their codebases and development culture.
woke provides an automated, configurable solution for detecting non-inclusive language directly in source code, with suggestions for alternatives and seamless integration into CI/CD pipelines. Its focus on raising awareness and enabling actionable change makes it a unique tool for promoting inclusivity in software development.
Detect non-inclusive language in your source code.
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The README provides dedicated documentation for integrating woke into CI/CD systems, enabling automated checks in workflows like GitHub Actions, which helps enforce inclusivity at scale.
woke supports custom rules via configuration files (e.g., .woke.yaml), allowing teams to define project-specific non-inclusive terms and replacements, as highlighted in the features.
It includes a pre-commit hook to catch issues before code is committed, promoting proactive inclusivity and reducing review overhead, as mentioned in the key features.
By suggesting inclusive alternatives for flagged terms, woke educates developers on better practices, aligning with its philosophy of raising awareness in the software community.
Since woke scans text files without semantic context, it may flag technical terms like 'master' in git branches or 'slave' in databases, leading to noise and manual review needs.
The tool focuses on English terminology, as evidenced by default rules for terms like 'whitelist/blacklist,' making it less effective for codebases with non-English comments or variables.
Setting up custom rules and exclusions requires editing YAML files and integrating into workflows, which can be complex for teams new to such tools, as implied by separate CI/CD installation docs.
Running woke on extensive text files or in CI pipelines may add processing time, though not explicitly noted, it's a common trade-off for static analysis tools that scan entire codebases.