Browse skills

Helps with building, debugging as part of testing, debugging, and quality assurance workflows.

Identifies and resolves memory corruption issues within Axiom codebases using advanced AI analysis and debugging techniques.

Identifies and suggests fixes for errors in hytopia game code, improving stability and functionality through automated analysis.

Automatically identifies and fixes code errors, bugs, and style violations based on expert linting rules.

Generates complete, end-to-end solutions—code, data, documentation—from high-level prompts for diverse tasks.

This Error Handler gracefully manages unexpected errors in your code, preventing crashes and providing informative feedback for debugging.

Automated debugging of QwibitAI models via intelligent error analysis and targeted code suggestions.

Identifies and suggests fixes for code errors based on detailed logs and error messages, improving test reliability.

Identifies and flags production code exhibiting debug mode anti-patterns like excessive logging or conditional debugging statements.

Helps with code generation, testing as part of agent workflows workflows.

Helps with code generation, testing as part of agent workflows workflows.

Provides testing guidance and assistance for software development workflows.

Automatically creates unit tests based on code comments and function signatures to improve test coverage.

Helps with testing, code generation as part of agent workflows workflows.

Helps with code generation, testing as part of agent workflows workflows.

Provides debugging guidance and assistance for software development workflows.

The Tdd Cycle skill automates testing feedback loops, rapidly iterating on code and ensuring faster, more reliable software development.

Automatically generates and runs small test cases based on code comments to verify functionality quickly.

Analyzes code execution to identify performance bottlenecks and suggest optimizations using detailed resource usage metrics.

Optimizes application performance through automated profiling, bottleneck identification, and targeted code recommendations.

Schlenks' systematic debugging analyzes code execution paths to pinpoint errors and suggest precise fixes using a structured approach.

Automatically identifies and suggests fixes for systematic errors in code based on test results and logs.

Suggests targeted debugging strategies based on error logs and code context to accelerate test failure resolution.

Systematically identifies and suggests fixes for code errors using detailed analysis and logical reasoning.