Skill UI

This skill orchestrates complex, behavior-preserving code refactoring. It is designed to improve code structure—such as extracting modules, eliminating duplication, or reducing nesting—without changing the core functionality. The process follows a rigorous, multi-phase gate system, ensuring existing tests remain green throughout the entire development lifecycle, culminating in a behavior-neutral commit.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.

This comprehensive guide details the complete Test-Driven Development (TDD) workflow for Spring Boot applications. It covers best practices for writing unit tests (JUnit 5, Mockito), web layer tests (MockMvc), integration tests (SpringBootTest), and persistence tests (DataJpaTest). It also instructs how to use Testcontainers for realistic database simulations and achieve high code coverage with JaCoCo, ensuring robust and maintainable codebases.

This skill automates the Test-Driven Development (TDD) repair loop. Given a failing unit test, it spawns a bounded, headless LLM agent to automatically modify the source code until the test passes. It closes the development loop by treating the test's pass/fail status as the definitive fitness function, ideal for CI failure triage and local TDD workflows.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

A comprehensive, multi-stage workflow for enhancing and structuring raw footage using advanced AI tools. It covers the entire pipeline—from initial recording (Screen Studio) through AI planning (Claude/Codex), deterministic cutting (FFmpeg), compositing (Remotion), asset generation (ElevenLabs/fal.ai), and final human polish (Descript/CapCut). Designed for users creating vlogs, tutorials, or polished corporate videos.

This tool allows users to create and manage advanced WASM agents within a secure, sandboxed environment. It ensures complete isolation during code execution, making it ideal for running untrusted code, experimenting with complex agent configurations, or developing portable applications that require guaranteed safety without accessing the host system resources.

The Witness toolkit provides cryptographic signing and verification for all documented code fixes. It ensures that specific fix markers remain present across all releases, and crucially, it maintains a temporal history (JSONL) allowing users to pinpoint the exact commit that introduced a regression. Ideal for robust CI/CD pipelines and release gating.

An advanced, secure skill leveraging OpenAI Codex and GPT models for end-to-end software development tasks. It supports autonomous code implementation, complex bug fixing, architectural analysis, and rigorous code/plan review. Operating in a controlled sandbox environment, it adheres to least privilege principles, allowing users to delegate entire coding workflows, from feature development to security auditing, without manual intervention on every step. Ideal for developers needing full-cycle automation assistance.

Provides a structured, multi-step workflow for addressing and fixing GitHub issues. This skill guides the user through the entire development lifecycle: understanding the bug, researching context, planning changes, implementing code, rigorous testing, and finally submitting a polished Pull Request using GitHub CLI commands.