Skill UI

This skill initializes a new autoresearch experiment. It guides the user through defining all necessary parameters, including the domain, target files, evaluation commands, metrics, and optimization direction. It is the essential starting point for automated optimization and performance benchmarking cycles.

AgentHub spawns parallel git worktree agents that compete on tasks, evaluates their outputs via metrics or LLM judges, and merges the top branch for optimizations, research, or content variations.

A comprehensive knowledge-base-first skill designed for deep literature reviews in advanced communication domains. Use it for topics like 4G/5G/6G, wireless networking, satellite communications, Wi-Fi, routing protocols, and cross-layer optimization. It systematically searches structured academic databases (IEEE, ACM, Zotero, etc.) to provide related work, survey papers, and landscape summaries.

This skill analyzes accumulated usage logs from a complex research harness (ARIS) to propose structural optimizations. It identifies areas for improvement in SKILL scripts, default parameters, workflow ordering, and reviewer prompts. It is designed to automate the meta-level engineering process, ensuring that system efficiency is continuously improved based on real-world usage data.

This skill provides a comprehensive, dual-engine audit for content, covering both Search Engine Optimization (SEO) and Answer Engine Optimization (AEO), along with readability metrics. It generates detailed scores (out of 100) and provides a severity-ranked list of issues (Critical, Important, Polish) with specific, actionable fix instructions. Use it before publishing to ensure content achieves optimal search performance and high engagement.

This skill analyzes a set of web pages to generate a prioritized roadmap for internal linking optimization. It identifies natural link opportunities, detects critical orphan pages, and warns against anchor text cannibalization risks. The output provides the exact anchor text, the optimal context sentence for placement, and classifies the link type (e.g., Cluster to Pillar), ensuring maximum authority flow and improved search engine visibility.

This skill generates multiple optimized variants for title tags, meta descriptions, and social media sharing tags (OG/Twitter). It provides three distinct styles (Benefit Lead, Question Hook, Social Proof) for each type, allowing users to A/B test copy to maximize click-through rate (CTR). Ideal for refining landing pages, blog posts, or any content requiring robust search engine optimization (SEO) and social media presence.

Provides a comprehensive audit of content quality, focusing on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals, readability, and structural integrity. Use this tool when reviewing content for SEO optimization, assessing academic citation readiness, or ensuring adherence to modern search engine and AI best practices.

Perform a comprehensive, deep-dive audit on a single URL, covering all critical aspects of Search Engine Optimization. Analysis includes on-page elements (title, headings, meta tags), content quality (readability, E-E-A-T), technical structure (schema, canonical, OG tags), image optimization, and core web vitals potential issues. Ideal for SEO audits and content quality checks.

Cirq is an open-source Python framework from Google Quantum AI for designing, simulating, and executing quantum circuits. It supports multiple hardware backends (IonQ, Pasqal, etc.) and includes comprehensive tools for noise modeling, circuit optimization, and parameterized experimentation, making it ideal for NISQ device development and advanced quantum computing research.

This skill functions as an expert GPU optimization engineer, transforming CPU-bound Python code into highly accelerated GPU kernels. It provides expertise across scientific computing, deep learning, and data analytics, covering core libraries like NumPy, pandas, and scikit-learn. Use cases include massive data parallelism, geospatial analysis, physics simulations, image processing, and advanced vector search on NVIDIA hardware.

Stable Baselines3 is a PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms, including PPO, SAC, DQN, and TD3. It offers a scikit-learn-like API for easily training RL agents, developing custom Gymnasium environments, and managing complex training workflows. Ideal for standard RL research, rapid prototyping, and advanced model optimization.