Skill UI

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 skill provides a structured methodology and Python implementation for monitoring and analyzing data leak sites (DLS) associated with ransomware groups. It helps security analysts track victim postings, extract threat intelligence on group tactics, assess sector-specific risks, and understand evolving ransomware threat landscapes for proactive defense.

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 facilitates autonomous, multi-round research review and refinement. It continuously submits work for assessment via external reviewers (Codex or manual), implements suggested fixes, and re-reviews until the designated positive threshold is met or the maximum number of rounds is reached. It manages state persistence and adjusts difficulty levels for rigorous, iterative quality assurance.

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.

This advanced skill automates the rigorous, iterative review and refinement of complex research topics or papers. It runs multi-round cycles, using an external LLM (MiniMax API) to provide continuous, constructive feedback. The process guides the user through review, fixes, and re-review stages until a positive assessment is achieved or the maximum round limit is reached. It includes robust state persistence for long-running academic workflows.

This skill deploys advanced anomaly detection systems for Operational Technology (OT) and Industrial Control Systems (ICS). It builds multi-dimensional baselines by analyzing SCADA communications, network traffic (Modbus, DNP3, OPC UA), and timing patterns. It detects deviations, rogue devices, and potential intrusions by profiling normal behavior, complementing signature-based IDS.

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.

A comprehensive guide and methodology for proactively hunting adversary abuse of legitimate cloud services (Azure, AWS, GCP, SaaS). This technique focuses on identifying Command and Control (C2), data staging, and exfiltration methods that leverage normal cloud functionality, making detection challenging.

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.

Tetragon, a CNCF project under Cilium, provides advanced, kernel-level runtime security observability and policy enforcement in Kubernetes clusters. By utilizing eBPF, it monitors process execution, file access, and network connections with minimal overhead, enabling detection and prevention of container escapes, unauthorized access, and suspicious activity.