Is this skill useful for reviewing AI-generated code?

Yes, it is specifically designed to catch 'plausible' code that LLMs sometimes generate which looks correct but doesn't actually execute the intended logic.

How does this skill detect fake implementations?

It uses a multi-phase approach: first scanning for suspicious patterns like empty catch blocks, then performing deep AST analysis to check for meaningful computational logic.

Can it detect tests that don't actually test anything?

Yes, it specifically scans for 'always-pass' patterns, such as tests that assert true equals true or lack meaningful expectations.

What exactly is 'theater code'?

Theater code is code that 'performs' correctness by following proper syntax and structure but fails to deliver actual functionality, such as functions with hardcoded returns or empty error handlers.

Does it support languages other than JavaScript and TypeScript?

The current patterns are optimized for JS/TS environments, but the multi-agent logic can be adapted to scan any C-style language for similar theater patterns.

Theater Code Detection

Name: Theater Code Detection
Author: DNYoussef

byDNYoussef

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Seguridad y Pruebas

Identifies non-functional code that appears complete but lacks operational logic to ensure production reliability.

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Theater Code Detection is a specialized quality assurance skill designed to identify 'performative' code—implementations that pass static analysis and look structurally sound but fail to deliver actual functionality. By systematically scanning for patterns such as empty catch blocks, no-op functions, and tests that always pass, this skill prevents deceptive or incomplete code from reaching production. It is particularly effective for reviewing AI-generated contributions, auditing third-party integrations, and acting as a final quality gate for mission-critical systems where 'fake' implementations could lead to silent failures.

Características Principales

Detection of 'always-passing' unit tests and fake assertions
Multi-agent coordination for high-confidence theater code reporting
Pattern-based scanning for empty error handlers and no-op functions
2 GitHub stars
Identification of production code containing mock imports or placeholders
Logic-to-comment ratio analysis to find superficial implementations

Casos de Uso

Pre-deployment quality gates to prevent empty error handling in critical paths
Security auditing to ensure security checks are not bypassable 'theater' implementations
Validating AI-generated pull requests for actual logic versus plausible-looking placeholders

Acerca de

Características Principales

Detection of 'always-passing' unit tests and fake assertions
Multi-agent coordination for high-confidence theater code reporting
Pattern-based scanning for empty error handlers and no-op functions
2 GitHub stars
Identification of production code containing mock imports or placeholders
Logic-to-comment ratio analysis to find superficial implementations

Casos de Uso

Pre-deployment quality gates to prevent empty error handling in critical paths
Security auditing to ensure security checks are not bypassable 'theater' implementations
Validating AI-generated pull requests for actual logic versus plausible-looking placeholders