Skill UI

This skill provides a structured methodology for detecting process injection techniques (MITRE ATT&CK T1055), which adversaries use for defense evasion and privilege escalation. It analyzes granular security logs, specifically utilizing Sysmon Event IDs 8 (CreateRemoteThread) and 10 (ProcessAccess), alongside source-target process relationship mapping to identify suspicious malicious behavior over general system activity.

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 skill automates the generation of high-quality, publication-ready figures and comparison tables from raw experimental data (CSV, JSON, etc.). It supports various plot types, including line plots, bar charts, scatter plots, heatmaps, and box plots, ensuring consistent style (DPI 300, PDF format) suitable for academic submissions. Users provide a figure plan and raw data, and the system generates the necessary code to visualize results.

This guide demonstrates how to build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across diverse sources. It covers the entire workflow, from processing disk images and various logs (MFT, registry, browser history) to generating standardized CSV, JSONL exports, and visualizing the results in tools like Timesketch for deep investigative analysis.

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.

The Stock Analyzer Skill provides comprehensive technical analysis for stocks and ETFs. It utilizes industry-standard indicators like RSI, MACD, and Bollinger Bands to generate actionable trading signals. Users can perform advanced technical comparisons, monitor portfolio strength, and identify chart patterns through natural language queries, eliminating the need for manual indicator calculation.

This skill provides institutional-depth equity analysis by combining public SEC EDGAR filings and real-time market data. It can generate structured, verdict-style memos, score stocks across four key pillars (Momentum, Stability, Financial Health, Upside), and perform side-by-side comparisons of two tickers, enabling data-driven investment decision support without requiring proprietary terminal access.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

This workflow bridges the gap between an abstract experiment plan and concrete, executable code. It reads structured experiment roadmaps (e.g., EXPERIMENT_PLAN.md), automatically generates or modifies necessary code scripts, handles prerequisite setup (like cloning base repositories), deploys the experiments to compute resources (GPU), and meticulously collects initial results. It includes optional steps for cross-model code review, ensuring robustness and correctness before execution. Ideal for moving from research hypotheses to measurable, running results.

A sophisticated, multi-stage workflow designed to generate publication-quality technical illustrations, such as architecture diagrams and complex method figures, for academic papers. It utilizes a strict, iterative loop supervised by an advanced AI (Claude) to refine layout, color compliance, and structural correctness, ensuring high fidelity and professional standards across multiple rendering passes.

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.