Skill UI

This tool is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

This guide addresses the critical difference between standard Node.js SHA3-256 and the Keccak-256 hash function required by Ethereum. Using the wrong hash function can silently corrupt calculated function selectors, event topics, storage slots, and derived addresses. It provides best practices and code examples for using dedicated libraries (like ethers, viem, web3.js) to ensure correct, Ethereum-compliant hashing in JavaScript and TypeScript environments.

This tool orchestrates the end-to-end process of building a brand-new feature. It automates the entire lifecycle, including initial research, planning, Test-Driven Development (TDD) implementation, code review, and gated commit. Use this when adding a capability that does not currently exist in the system.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

repo-scan is a powerful tool designed to audit large, complex, cross-stack codebases (C++, Java, iOS, Web). It classifies every file into project code, third-party dependencies, or build artifacts. Key features include detecting embedded libraries (e.g., FFmpeg, OpenSSL) and providing a practical 4-stage assessment (Core Asset / Extract / Refactor / Deprecate) via an interactive HTML report. Essential for managing monorepos and planning major refactoring efforts.

This guide provides comprehensive patterns for writing reliable and maintainable tests in Rust, adhering to the TDD methodology. It covers unit testing, integration testing, asynchronous programming with Tokio, property-based testing, mocking, and robust error/panic handling. Ideal for developers looking to elevate their Rust code quality and coverage.

The Safety Guard skill is designed to prevent destructive actions when working in production systems or when autonomous agents are executing tasks. It offers three protection modes (Careful, Freeze, and Guard) to monitor dangerous commands, lock specific directories, and ensure code changes are confined to intended areas, enhancing system stability and security.

This tool helps development teams identify gaps in their current test coverage. It analyzes code paths and detects areas that lack adequate unit or integration tests, generating actionable suggestions for improvement. Use it when assessing code quality after a feature implementation, or when a user needs to know which parts of the codebase require further testing.

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 powerful skill leveraging OpenAI Codex/GPT for autonomous, hands-off code generation, implementation, and complex task execution. It operates in sandboxed modes (Read-Only, Workspace-Write) to allow developers to refactor, fix bugs, and build features with minimal user intervention. Ideal for complex programming tasks, code reviews, and structured workflow automation.