Detect LLM reward hacking patterns in .NET code changes.
npx skills add https://github.com/seiggy/lucia-dotnet --skill dotnet-slopwatch-seiggy
Please help me install this Agent Skill. Skill: dotnet-slopwatch Source: https://github.com/seiggy/lucia-dotnet/tree/main/.github/skills/slopwatch Command: npx skills add https://github.com/seiggy/lucia-dotnet --skill dotnet-slopwatch-seiggy
This Skill prevents LLMs from introducing "slop" into your .NET codebase, ensuring code quality and preventing the masking of underlying issues.
Run slopwatch analyze after any LLM-generated code modification to detect new issues.
High-intent search queries and answers about installing and using this skill.
Detect LLM reward hacking by scanning .NET source, project, and test files for slop patterns like disabled tests, warning suppressions, and empty catch blocks. This identifies shortcuts LLMs take to force builds to succeed without genuine fixes.
LLM slop in .NET codebases refers to low-quality code patterns introduced by AI, such as disabled tests, arbitrary delays, and project-level warning suppressions. These patterns mask underlying issues to make tests pass or builds succeed artificially.
Scan for disabled tests and suppressed warnings by running an automated analysis tool on your modified C# files. The process flags newly introduced instances of empty catch blocks and project-level suppressions to prevent masking build failures.
Yes, you can integrate automated code quality checks into CI/CD pipelines for .NET projects. The analysis hooks into your pipeline to automatically scan C# source and test files for slop patterns before merging LLM-generated changes.
Yes, this slop detection tool works with Claude code hooks. It integrates directly to automatically analyze generated C# code changes for reward hacking patterns like arbitrary delays and empty catch blocks before execution.
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