Skill UI

This skill generates and ranks publishable research ideas based on a broad direction provided by the user. It systematically validates concepts through pilot experiments and model reviews, optimizing for GPU efficiency and novelty. Suitable for brainstorming sessions, academic exploration, and identifying workable research paths.

This skill provides a structured methodology for mitigating alert fatigue within Security Operations Centers (SOCs). It guides users through advanced techniques, including implementing Risk-Based Alerting (RBA) to replace high-volume, low-fidelity alerts with consolidated risk scores. It emphasizes quantifying alert quality by analyzing True Positive (TP) and False Positive (FP) rates, and systematically tuning detection rules to maintain analyst effectiveness.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).

This skill guides the implementation of Security Orchestration, Automation, and Response (SOAR) workflows using platforms like Splunk Phantom. It automates the entire incident lifecycle, including alert triage, IOC enrichment (via VirusTotal, CrowdStrike), containment actions, and ticket creation (ServiceNow). Ideal for SOC teams needing to reduce Mean Time To Respond (MTTR) and standardize complex security procedures across multiple tools.

Implements a comprehensive incident ticketing system that connects SIEM alerts to major ITSM platforms like ServiceNow, Jira, or TheHive. This solution formalizes incident tracking, automates ticket creation, and manages critical processes such as Service Level Agreement (SLA) tracking, complex escalation workflows, and compliance documentation throughout the entire incident lifecycle.

A comprehensive guide detailing Next.js 16+ usage with Turbopack. Turbopack, written in Rust, is an incremental bundler designed to drastically speed up development startup and Hot Module Replacement (HMR) by utilizing file system caching. This document guides developers on best practices, differentiating between the default Turbopack usage and when to revert to Webpack for debugging or compatibility.

Executes a structured VC valuation workflow that ingests SaaS or startup metrics, runs DCF, comparable revenue multiples, and SaaS health models via Python, and synthesizes a defensible valuation, runway, and capital-efficiency narrative for users planning fundraising or benchmarking.

This tool audits the token consumption across all components (agents, skills, rules, MCP servers) within a Claude Code session. It analyzes token overhead, identifies bloat (e.g., overly long descriptions, redundant components), and generates a detailed context budget report. Use it to maintain optimal context window usage, improve performance, and prevent context saturation.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

A specialized skill for generating production-ready, SEO-optimized visual assets. It handles various use cases including OG social cards, blog hero images, product visuals, and infographics. The system ensures outputs adhere to best practices like specific aspect ratios, schema markup, and file naming conventions, making content publishable and discoverable by search engines.

Analyze a codebase to assemble an interactive knowledge graph showing architecture, components, and relationships, enabling full rebuilds, incremental updates, or audits with optional auto-update control.

Launches an interactive web dashboard designed to visualize the knowledge graph derived from a target codebase. This tool helps developers gain a high-level, structural understanding of complex projects by mapping out relationships, module dependencies, and the overall knowledge network of the code, making maintenance and onboarding significantly easier.