Skill UI

A comprehensive, end-to-end playbook designed for software engineers and AI agents. It provides a structured methodology to safely introduce machine learning capabilities into existing, non-ML codebases. The process covers critical stages: problem framing, data readiness, architectural decoupling (using dedicated APIs/services), baseline model implementation, and final handoff to MLOps practices (CI/CD, monitoring).

Repo-scan is a powerful tool designed for comprehensive auditing of large and complex codebases, especially monorepos. It performs cross-stack analysis across C++, Android, iOS, and Web technologies. Key features include classifying files (core code, third-party, build artifacts), identifying known libraries, and assessing code modules into four stages: Core, Extract, Refactor, or Deprecate. Essential for planning major refactoring efforts or inheriting legacy systems.

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 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 helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

Scan entire codebases or specific changes, audit repositories for security issues, and generate verified patch files. Supports scanning whole repositories, branch diffs, or individual commits. Each finding is verified by an agent panel before reporting. Suggested patches are saved as files for manual review and application. Works in auto mode for streamlined security auditing workflows.

Use the Codebase Memory graph to explore unfamiliar code, map architecture, find symbols and relationships, trace callers and data flow, assess impact, and audit dead or complex code, with Scout, Verify, and Auditor evidence levels.

This skill offers two complementary workflows for locally checked-out codebases. Documentation mode creates a definitive, cited architecture document ideal for onboarding and system research. Modernization mode then builds upon this documentation, generating a phased, safety-laddered plan to upgrade, migrate, or rewrite legacy systems. It emphasizes local-first analysis for maximum accuracy.

This guide outlines best practices for packaging complex research artifacts—including simulators, codebases, and datasets—to maximize credibility and reproducibility. It is essential for authors publishing in venues like INFOCOM where formal artifact evaluation tracks are not mandatory. Learn how to structure your submission to build reviewer trust and ensure your findings can be independently verified.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.

This guide provides a comprehensive framework for authors responding to major journal revisions. It teaches how to structure a technical, point-by-point rebuttal, systematically closing proof gaps, strengthening assumptions, incorporating missing baselines, and preparing the entire codebase and data for professional reproducibility review.

This tool provides a comprehensive, multi-faceted audit of a codebase's health. It scores the project across critical areas like security vulnerabilities, exposed secrets, dependency risks, and structural integrity. Ideal for senior engineers performing pre-commit or pre-release quality assessments to identify weak spots and generate a prioritized remediation plan.