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jacoco-badge-generator
Coverage badges, and pull request coverage checks, from JaCoCo reports in GitHub Actions
There are two primary ways of implementing a GitHub Action: JavaScript Actions and Container Actions. The latter of which enables implementing Actions in any language via a Docker container. My language of choice for implementing GitHub Actions is Python. The purpose of most of these actions is to produce files (e.g., jacoco-badge-generator produces test coverage badges as SVGs, and generate-sitemap produces an XML sitemap) or to edit files in some way (e.g., javadoc-cleanup can insert canonical links and other user-defined elements into the head of javadoc pages). However, all of these also produce workflow step outputs. For example, generate-sitemap has outputs for the number of pages in the sitemap, and the number of pages excluded from the sitemap due to noindex or robots.txt exclusions; and jacoco-badge-generator has workflow step outputs for the coverage and branches coverage percentages if a user had some reason to use those in later steps of their workflow.
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Nutrient
Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers. Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
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I use GitHub Actions to automate a variety of things in nearly all of my repositories, such as running a build and tests during pull-requests and pushes, deploying artifacts to Maven Central, etc for my Java libraries, or to PyPI for a couple Python projects, building my personal website with my custom static site generator, among a variety of other tasks. In addition to using GitHub Actions for workflow automation, I also develop and maintain a few Actions (all implemented in Python), including jacoco-badge-generator, user-statistician, javadoc-cleanup, and generate-sitemap.
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javadoc-cleanup
Create mobile-friendly documentation sites by post-processing javadocs in GitHub Actions
There are two primary ways of implementing a GitHub Action: JavaScript Actions and Container Actions. The latter of which enables implementing Actions in any language via a Docker container. My language of choice for implementing GitHub Actions is Python. The purpose of most of these actions is to produce files (e.g., jacoco-badge-generator produces test coverage badges as SVGs, and generate-sitemap produces an XML sitemap) or to edit files in some way (e.g., javadoc-cleanup can insert canonical links and other user-defined elements into the head of javadoc pages). However, all of these also produce workflow step outputs. For example, generate-sitemap has outputs for the number of pages in the sitemap, and the number of pages excluded from the sitemap due to noindex or robots.txt exclusions; and jacoco-badge-generator has workflow step outputs for the coverage and branches coverage percentages if a user had some reason to use those in later steps of their workflow.
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There are two primary ways of implementing a GitHub Action: JavaScript Actions and Container Actions. The latter of which enables implementing Actions in any language via a Docker container. My language of choice for implementing GitHub Actions is Python. The purpose of most of these actions is to produce files (e.g., jacoco-badge-generator produces test coverage badges as SVGs, and generate-sitemap produces an XML sitemap) or to edit files in some way (e.g., javadoc-cleanup can insert canonical links and other user-defined elements into the head of javadoc pages). However, all of these also produce workflow step outputs. For example, generate-sitemap has outputs for the number of pages in the sitemap, and the number of pages excluded from the sitemap due to noindex or robots.txt exclusions; and jacoco-badge-generator has workflow step outputs for the coverage and branches coverage percentages if a user had some reason to use those in later steps of their workflow.
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Chips-n-Salsa
A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms
The complete workflow file that this example is derived from is maven-publish.yml.
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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