Skill UI

This skill provides comprehensive tracking and visualization for Security Operations Center (SOC) performance. It calculates critical metrics such as Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), and alert quality ratios using SIEM data. Ideal for SOC leadership, compliance auditing, and continuous process improvement initiatives needing quantifiable security posture reporting.

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.

Generate publication-quality figures and comparison tables from experiment results for academic papers. Automatically selects plot types from data and produces vector PDFs at 300 DPI.

This skill guides the systematic management of Indicators of Compromise (IOCs). It covers the entire lifecycle, from initial discovery and validation through enrichment (e.g., VirusTotal, Shodan) and deployment to SIEM/IDS. Key features include confidence decay over time, false positive tracking, and automated retirement policies, ensuring the threat intelligence database remains high-quality and actionable.

This skill outlines the systematic process of integrating new data sources (e.g., Firewalls, EDR, Cloud, AD) into a SIEM platform. It covers planning, configuring collectors, building parsers, normalizing data fields (e.g., CIM), and validating data quality to enhance security monitoring and incident detection capabilities.

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.

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.

Processes meeting transcripts and recordings to normalize speakers, log metadata, and generate actionable coaching on speaking balance, conflict avoidance, filler usage, and question quality whenever users reference their meetings or tools like Otter or Zoom.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

This expert tool provides deep, systematic auditing of datasets to assess completeness, consistency, accuracy, and structural validity. It performs full data profiling, detects anomalies (outliers), analyzes missing data mechanisms (MCAR/MAR/MNAR), and calculates a Data Quality Score (DQS). Use it when validating data for model training, debugging pipelines, or establishing ongoing monitoring checks.

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

This comprehensive guide outlines best practices for developing high-quality, idiomatic, and maintainable enterprise applications using Kotlin and Spring Boot. It covers architectural patterns, dependency injection using primary constructors, managing state with data classes, implementing robust RESTful APIs, handling transactions, and leveraging Kotlin features like null safety and Coroutines for asynchronous programming. It also provides guidance on modern testing strategies (Kotest, Testcontainers).