Detect lie-code patterns in codebases using static grep-like scans.
npx skills add https://github.com/stronghuni/FindLie --skill lie-scan
Please help me install this Agent Skill. Skill: lie-scan Source: https://github.com/stronghuni/FindLie/tree/main/lie-scan Command: npx skills add https://github.com/stronghuni/FindLie --skill lie-scan
Quickly detects lie-code indicators in a codebase without executing code, helping teams catch incorrect or deceptive patterns early in development.
Run the lie-scan skill to perform a fast static scan for lie patterns and report findings in under 30 seconds.
High-intent search queries and answers about installing and using this skill.
Run static pattern-based scanning to detect lie-code patterns like mock data and empty catches in a JavaScript codebase without executing code. This fast grep-like scan surfaces deceptive patterns early for prioritized fixes.
Yes, static lie-code scanning integrates into CI-CD pipelines using CI-friendly mode with machine-readable exit codes and structured findings. This enables seamless pipeline automation and early issue detection.
Lie-code refers to incorrect or deceptive programming patterns like stubs, dead code, and duplicated files. Static pattern detection identifies it by scanning codebases with grep-like rules to surface these indicators quickly without executing code.
Yes, this static pattern scanner works with Python and TypeScript files, along with JavaScript and other common stacks. It performs fast grep-like scans across multiple languages to detect deceptive patterns consistently.
The best way to run a pre-commit check for placeholder URLs and dead code is using a fast static pattern scanner that delivers structured findings in under 30 seconds. This catches and surfaces deceptive patterns before committing changes.
Static scanning may report false positives for duplicated files because it uses grep-like pattern matching without executing code or analyzing runtime context. It flags suspected lie-code indicators early, requiring developers to review structured findings manually.
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