Skill UI

This specialized tool analyzes Git diffs against a stated development intent to detect scope creep. It identifies overly broad pull requests, unrelated files, dependency additions, public API renames, or configuration changes. It guides users on whether changes should be kept, split into separate commits, or if the connection requires explicit justification, ensuring clean and focused code merges.

This comprehensive guide details how to architect a lengthy, complex academic paper into a single, highly navigable PDF. It establishes a two-tier document model, separating self-contained 'Front Matter' from verifiable 'Back Matter.' Learn best practices for structuring proofs, ensuring dependency order, and implementing critical navigation aids (like claim indexes and TOCs) so reviewers can follow the argument flow seamlessly, even without traditional supplementary files.

A comprehensive guide to vcpkg, a native C/C++ package manager. It covers project setup, dependency management, and cross-compiling for robust builds. The guide details best practices for Manifest mode, CMake integration, and Visual Studio setup, ensuring reproducible and isolated build environments.

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.

This advanced skill specializes in tracing the complete provenance of software artifacts across the entire development lifecycle. It is designed for deep security analysis of CI/CD pipelines, identifying dependency drift, build tampering, package origin, and determining the final artifact consumed at runtime. Use it when investigating integrity vulnerabilities in complex release flows.

This local developer tool inspects dependency manifests (requirements.txt, pyproject.toml, package.json) for surface-level issues like standard-library shadowing, unpinned dependencies, and conflicts. It provides plain-language explanations and suggested fixes, helping developers debug dependency rot before deeper installation attempts.

Automates the creation of isolated Git worktrees for safe and efficient feature development. This process handles branch validation, optimizes dependency installation (e.g., symlinking node_modules), runs background verification tests, and provides guidance for database isolation, ensuring developers always work in a clean, separated environment.

A powerful, dependency-free CLI tool designed for programmatically creating, reading, analyzing, and modifying Microsoft Office files (.docx, .xlsx, .pptx). It supports deep DOM editing (L2) and raw XML manipulation (L3), allowing users to extract structured data, validate schemas, and perform complex document automation tasks from a single binary.

This tool simulates a senior application security engineer, performing a comprehensive, multi-phase audit. It checks for exposed secrets, prompt injection vectors, dependency vulnerabilities (supporting multiple languages), and critical configuration misconfigurations. It provides a scored, prioritized remediation report, ensuring a robust security posture before deployment.

This framework, known as Via Negativa, guides iterative improvement by prioritizing removal and simplification over adding new features. It mandates that before introducing any new complexity, dependency, or layer, one must exhaust all possibilities of eliminating redundant, harmful, or nonessential elements. It emphasizes evidence-based decision-making and risk assessment, ensuring that new additions are truly necessary after thorough subtraction passes.

Adds existing Dataverse tables to Power Apps code apps with generated TypeScript models and services, or creates new Dataverse tables via API. Supports schema planning, dependency ordering, CRUD, and file/image uploads. Use when connecting to Dataverse or querying table data.

Seeds Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order.