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*[Shippie](https://github.com/mattzcarey/shippie) - Code review, secrets detection, bug fixes and more. TypeScript + Bun.
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*[Aetherr Agency DeepDive](https://github.com/Aetherr-Agency/DeepDive/) - "Analyze the quality of your test files, providing insightful feedback and suggestions for improvement."
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*[shippie](https://github.com/mattzcarey/shippie), Shippie uses Large Language Models to review code in your CI/CD pipeline.
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## Continuous Code Commenting
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*[SoftwareTesting AI](https://softwaretesting.ai/) - A code quality platform that identifies coverage gaps and suggests how to resolve them
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*[DiffBlue](https://www.diffblue.com/ci-pipeline/) - Automate continuous unit testing at scale in CI
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## Programming Frameworks
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Any library that can invoke an LLM inference service could be used to create a workflow.
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Some of them have builtin support for GitHub Actions and/or GitHub Models.
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### YAML (GitHub Actions Yaml)
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*[actions/ai-inference](https://github.com/actions/ai-inference): a standard GitHub Action for invoking LLMs direct from YAML
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### Shell Scripting:
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*[llm](https://llm.datasette.io/) and [llm-github-models](https://github.com/tonybaloney/llm-github-models) to run LLMs in shel scripting
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*[ast-grep](https://ast-grep.github.io/): A tool for analyzing and transforming code using abstract syntax trees (ASTs)
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### JavaScript/TypeScript
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*[GenAIScript](https://https://microsoft.github.io/genaiscript/), a scripting language for GitHub Actions that simplifies the use of AI models and tools in workflows
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### Markdown
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*[shippie](https://github.com/mattzcarey/shippie), uses Markdown based rules to define LLM-powered code reviews
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