Skill UI

This sophisticated workflow guides users from a broad research concept or initial topic to a fully validated, actionable, and pilot-tested research idea. It orchestrates a multi-stage pipeline, including literature surveys, novelty checks, detailed idea generation, critical review, and the creation of a refined experimental plan, ensuring the output is a robust proposal ready for implementation.

This skill implements the Diamond Model framework, providing a structured approach to analyzing complex cyber intrusions. It allows users to programmatically classify and correlate events based on four core elements: Adversary, Capability, Infrastructure, and Victim. Key functionalities include building activity threads, identifying pivot points, and generating comprehensive, pivot-ready threat intelligence reports.

This comprehensive skill guides the user through monitoring various running experiments across different platforms (SSH, Vast.ai, Modal). It provides structured steps to check running processes, collect logs, extract structured JSON results, and pull detailed metrics from Weights & Biases (W&B). Finally, it summarizes findings, compares against baselines, and suggests next steps.

This skill implements a multi-factor scoring model to assess the criticality of IT assets. By weighting factors such as business function impact, data sensitivity, regulatory scope, and network exposure, it assigns a risk tier. This tier is then used to dynamically adjust the Service Level Agreements (SLAs) for vulnerability remediation, ensuring that organizations prioritize patching efforts on their most critical 'crown jewel' systems first.

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

This skill automates deep packet capture (PCAP) analysis using tshark and pyshark. It is invaluable for security assessments and incident response, enabling the extraction of protocol statistics, identification of suspicious network flows (e.g., port scanning, beaconing), and comprehensive extraction of Indicators of Compromise (IOCs), including IPs, domains, and URLs. It also specializes in detecting complex anomalies like DNS tunneling.

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 orchestrates the entire academic research lifecycle, transforming a broad research direction into a polished, publishable paper. It sequentially manages idea discovery, literature review, experimental implementation, cross-model review, and final manuscript writing, supporting autonomous or manually guided advanced research workflows.

This comprehensive guide provides a detailed workflow for parsing Windows LNK shortcut files in a digital forensics context. It extracts critical artifacts including target paths, timestamps (creation, access, modification), volume serial numbers, and machine identifiers. It is essential for reconstructing user file access history, tracking accessed files on network shares, and building robust forensic timelines.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.