Skill UI

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.

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.

RecallMax dramatically enhances AI agent memory capabilities by cleanly injecting massive external contexts (500K-1M tokens) without hallucination drift. It automatically summarizes lengthy conversations, preserving tone, intent, and key facts. Furthermore, it compresses multi-turn interaction histories into high-density token sequences, ensuring agents maintain perfect recall across complex and long-running sessions.

An advanced reliability wrapper for AI agents that monitors, manages, and recovers from tool call failures. It automatically handles common issues such as truncated JSON responses, API timeouts, rate limits, and mid-chain process breakdowns. By implementing sophisticated failure classification, checkpointing, and learning mechanisms, Guardian ensures complex, multi-step agent workflows remain stable and resilient.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

This skill automates the scholarly process of refining a generated academic paper. It executes an iterative loop (Review → Fix → Recompile) using advanced LLMs (like GPT-5.5) over multiple rounds. It is designed not for primary research, but specifically for polishing writing quality, correcting theoretical inconsistencies, improving presentation, and enhancing narrative flow after the initial draft is complete. It helps elevate a paper from a draft to a polished submission.

This skill automates the research improvement process by creating a closed loop: Review -> Implement Fixes -> Re-review. It leverages any OpenAI-compatible LLM API to iteratively refine research work until a positive assessment is reached or a maximum round limit is hit. Ideal for academic or technical project refinement.

Utilizes User and Entity Behavior Analytics (UEBA), statistical baselines, and machine learning models to monitor authentication logs. This skill identifies sophisticated security threats such as impossible travel, credential stuffing, brute force attacks, and compromised accounts in real-time. Essential for security operations and identity access management.

This guide provides advanced detection methods and threat hunting queries (KQL/SPL) to identify abuse of Azure service principals. It covers techniques such as privilege escalation, credential compromise, unauthorized role assignment, and enumeration, essential for SOC analysts and security teams monitoring Microsoft Entra ID.

A powerful academic tool designed for researchers. Given a broad research direction, it systematically surveys the current literature (landscape survey), identifies knowledge gaps, and generates multiple concrete, high-potential hypotheses. It then validates and ranks these ideas to propose novel, publishable research projects.

This comprehensive skill automates the entire process of generating novel, robust, and benchmark-grounded research ideas in robotics and embodied AI. It guides users from a broad initial concept to concrete, actionable project proposals by chaining rigorous literature surveys, novelty verification, and critical reviews. The workflow enforces robotics constraints, prioritizing simulation-first, falsifiable, and feasible real-world applications.

This skill guides the implementation of network traffic baselining using historical NetFlow/IPFIX data. It leverages Python's pandas library to compute detailed statistical profiles, including hourly/daily distributions, per-host metrics, and protocol ratios. Anomalies are robustly detected using z-score and Interquartile Range (IQR) methods, assisting SOC analysts in identifying unusual activities like data exfiltration or beaconing.