Skill UI

Coordinates Exa MCP-driven web_search_exa and get_code_context_exa calls for up-to-date web results, code snippets, and company or people intel whenever the user requests research, competitive analysis, or development context.

A comprehensive skill for detecting advanced malware techniques, including DLL injection, process hollowing, APC injection, and thread hijacking. Utilizes memory forensics (Volatility), API monitoring (Sysmon), and behavioral analysis to identify malicious code artifacts and suspicious process activities in a deep defense context.

This skill utilizes the Shodan API to perform comprehensive IP reputation analysis. It enriches IP addresses by identifying open ports, running services, known vulnerabilities (CVEs), and hosting context. It is crucial for threat intelligence enrichment, incident triage, and security assessments, helping security professionals assess the risk level and potential compromise of exposed infrastructure.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

A comprehensive set of specialized tools for deep interaction with source codebases. This context-mode is mandatory for advanced tasks such as running code snippets, analyzing files, batch processing repository commands, indexing knowledge, searching content, and fetching web data. It ensures that interactions are context-aware and highly efficient, moving beyond basic file listing or general chat prompts.

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 is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Automates daily planning by integrating with your calendar (e.g., Outlook via WorkIQ). It pulls your schedule, cross-references open tasks, analyzes workspace context, and classifies every meeting (Customer, Deep Work, Learning, etc.). The tool detects scheduling conflicts, identifies time zone issues, and generates a structured HTML prep file with proactive recommendations to maximize focus and productivity for the next day.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

This skill analyzes the implementation gap by comparing approved feature requirements against the existing codebase, architecture, and context. It helps identify missing capabilities, potential integration challenges, and necessary technical research areas. The output provides multiple viable implementation options and a comprehensive analysis report, guiding the transition from requirements definition to technical design.