Detect reward-hacking shortcuts in .NET code diffs.
npx skills add https://github.com/akoken/dotfiles --skill dotnet-slopwatch-akoken
Please help me install this Agent Skill. Skill: dotnet-slopwatch Source: https://github.com/akoken/dotfiles/tree/main/config/.copilot/skills/dotnet-slopwatch Command: npx skills add https://github.com/akoken/dotfiles --skill dotnet-slopwatch-akoken
This Skill helps teams detect and prevent reward-hacking shortcuts in .NET code changes, such as disabled tests, suppressed warnings, and empty catch blocks, ensuring code quality and reliable tests.
Install Slopwatch for .NET tooling, configure the slopwatch.json, and run slopwatch analyze after edits to identify and address potential shortcuts.
High-intent search queries and answers about installing and using this skill.
You can detect disabled tests and suppressed warnings in .NET code changes by analyzing code diffs for unsafe patterns like disabled tests, empty catch blocks, and warning suppressions, enforcing proper fixes rather than quick wins.
LLM reward hacking in C# projects involves taking shortcuts like disabling tests or adding artificial delays. Static analysis catches this by scanning code diffs after modifications to enforce workflow rules and require proper fixes.
You can integrate automated code quality checks for LLM generated C# code by running diff analysis after each PR to validate changes, catching unsafe patterns like arbitrary delays and empty catch blocks before merging.
Yes, you can use it to analyze project files and test suites in dotnet. It is applicable to C# projects and analyzes code diffs after every modification to catch unsafe patterns, supporting configuration through a slopwatch.json file.
You configure analysis rules for a .NET repository by setting up a slopwatch.json configuration file in the .slopwatch directory, defining the static analysis and workflow rules to enforce proper fixes for detected unsafe patterns.
The limitation of using diff analysis to prevent reward hacking in C# is that it primarily targets specific unsafe patterns in modified code, meaning pre-existing issues in unchanged code may not be detected without a full baseline scan.
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