Skill UI

A comprehensive guide derived from C++ Core Guidelines, enforcing modern, safe, and idiomatic practices for C++17, C++20, and C++23. It emphasizes crucial principles like RAII, type safety, immutability, and proper resource ownership using smart pointers. Use this when writing new features, reviewing existing code, or making architectural decisions to ensure code quality and consistency.

This tool performs a deep, recursive, and parallel investigation, building a comprehensive graph-structured dossier around a specified seed entity. It expands outward across diverse sources, including web search, internal memory stores, codebases, and knowledge graphs. It captures all connected entities and the provenance of every claim, making it ideal for mapping the full context or relationship network of a subject.

Generates professional PNG icon image sets for the Xcode Asset Catalog, supporting both massive open-source collections via Iconify API and native Apple symbols via SF Symbols. Use this tool to streamline the creation, search, and replacement of app icons, ensuring consistency in style, size, and color across your iOS project.

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.

This skill manages the secure transition of private internal projects to public open-source releases. It executes a three-stage pipeline: Forking (removing secrets), Sanitizing (verifying clean code), and Packaging (generating required documentation like README, setup.sh, and CLAUDE.md). It ensures code safety and readiness before public deployment.

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.

A utility designed to generate high-dimensional vector embeddings (384-dim) from various sources including text, code, or documents. This process converts raw data into numerical representations optimized for semantic search, similarity comparison, and clustering, utilizing ONNX and HNSW indexing for efficient retrieval.

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.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

Reconcile code, docs, rules, memory, and workspace residue so the next session starts from one current source of truth. Best for project handoff, stale documentation, and closing out knowledge drift.

This skill guides authors in determining the suitability of their manuscripts for the journal Genome Biology. It details the journal's scope, which requires integrating large-scale genomic or epigenomic data with novel computational methods or statistical frameworks. It emphasizes that successful submissions must provide both methodological advances and meaningful biological insights, adhering strictly to standards like open-source code, public data deposition (GEO, SRA, ENCODE), and rigorous benchmarking.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.