Browse skills

Automatically identifies, fixes, and debugs code errors based on provided error messages and context.

Inspects v1-io data input/output streams to identify errors and bottlenecks during testing workflows.

Identifies and suggests fixes for errors in QA test scripts using AI analysis and code suggestions.

Identifies and suggests fixes for code errors using advanced analysis and pattern recognition during automated testing.

Scans websites & documents for inconsistencies, errors, and missing information like a meticulous raccoon.

Reviews Python code for style, errors, and potential improvements using ratacat's Kieran model, ensuring quality and best practices.

Analyzes Python code for potential issues like style violations and bugs, improving readability & reducing errors.

Analyzes Rust code for linear type usage errors and suggests safe refactoring strategies to prevent memory unsafety.

Reviews TypeScript backend code for errors, style inconsistencies, and potential performance improvements using ratacat's Kieran.

This skill leverages the Scala type system to analyze code and identify potential errors or improve code quality for enhanced reliability.

The Type Checker verifies code for potential type errors, ensuring robust and reliable applications by catching issues early on.

Analyzes Next.js code for best practices, performance optimizations, accessibility issues, and potential errors.

Analyzes complex Rust compiler errors, suggests fixes, and explains underlying causes for improved code quality.

Safely manage Rust data lifetimes and prevent memory errors using huiali's rust-pin pinning utility.

Safely manages complex Rust unsafe code blocks, minimizing errors and ensuring memory safety within backend systems.

Generates Java code snippets, explains concepts, and debugs errors based on your specifications, powered by ar4mirez.

Reviews Clojure code for style, common errors, and potential improvements using microck's established best practices.

Poteto-debugging identifies subtle software errors by simulating unpredictable user behavior and data anomalies.

Identifies and suggests fixes for Reinforcement Learning Model training errors based on logs and metrics.

Automatically identifies and suggests fixes for code errors in iliaal projects, streamlining debugging workflows.

Identifies and suggests fixes for code errors using advanced analysis and debugging techniques.

Ricardogomes' guided-debugging helps testers pinpoint errors by suggesting fixes and explaining code behavior step-by-step.

Reports detailed query results and errors from Supabase databases directly within httprunner test execution flows.

Automates cloud build processes, generating configurations and troubleshooting errors for faster deployments.