Skill UI

This skill guides the creation of high-quality, finished cyber threat intelligence reports. It tails content for specific audiences—from C-suite executives (strategic risk) to SOC analysts (tactical IOCs). Use it when synthesizing raw data into actionable intelligence products, generating threat briefings, or conducting post-incident assessments, ensuring adherence to industry standards like TLP and NIST.

This skill provides structured methods for detecting credential stuffing attacks by analyzing authentication logs. It focuses on identifying complex patterns such as login velocity anomalies, high IP diversity, password spray techniques, and suspicious geographical distributions of failed logins. It is essential for SOC analysts conducting threat hunting or building robust detection rules against account takeover attempts.

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 performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

This skill uses advanced AI and LLM capabilities to correlate fragmented Open Source Intelligence (OSINT) data. It ingests findings from diverse sources—including username enumeration, email lookups, social media, domain records, and breach databases—to generate unified, intelligence-grade profiles. It provides link analysis and confidence scoring, transforming raw data into actionable threat intelligence for target profiling.

An advanced tool for network intelligence that calculates weighted graph scores to map the optimal path to a target. It identifies potential introducers, measures 'bridge value' across connections, and helps determine whether a connection requires a warm introduction or direct outreach on platforms like X and LinkedIn. Ideal for users needing the raw ranking methodology.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

NetworkX is a powerful Python library used for creating, manipulating, and analyzing complex networks and graphs. It supports graph structures (directed/undirected, multi-edges) and provides extensive algorithms for tasks such as finding shortest paths, calculating centrality measures (e.g., PageRank), detecting communities, and simulating various network types (e.g., scale-free, small-world). Ideal for social science, biology, and transportation network modeling.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

This skill generates a sophisticated, single-screen consumer dashboard mimicking a high-end lifestyle publication's dating column. It moves beyond simple swiping mechanics to visualize complex social metrics, mutual matches, and connectivity trends using editorial typography and restrained design. Ideal for concepts requiring data-driven storytelling about personal or community relationships.

A comprehensive 13-agent pipeline designed for rigorous, domain-agnostic academic research. It guides the user through the entire research lifecycle, from formulating precise research questions (Socratic mode) and designing methodologies, to conducting systematic literature reviews, performing meta-analyses, source verification, bias assessment, and compiling final APA 7.0 reports. Ideal for academics, researchers, and students needing deep, evidence-based insights on any complex topic.