Skill UI

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

This tool parses API Gateway access logs from various providers (AWS, Kong, Nginx). It performs statistical analysis using pandas to detect critical security threats such as Broken Object Level Authorization (BOLA/IDOR), credential stuffing, rate limit bypass attempts, and injection vulnerabilities. Ideal for SOC analysts or security engineers investigating API abuse and building advanced threat detection rules.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.

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 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.

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.

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 skill provides programmatic access to comprehensive global financial market data via the Alpha Vantage API. Users can retrieve time-series data, including historical prices (OHLCV) for equities, options, forex, commodities, and crypto. It also provides access to fundamental metrics (e.g., P/E ratio, market capitalization), economic indicators (e.g., GDP, CPI), and over 40 technical indicators (e.g., RSI, MACD), making it ideal for quantitative analysis and financial modeling.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Automates GA4 reporting and property management via Composio’s Rube MCP, covering account/property discovery, standard/batch/pivot/funnel reports, and key event workflows so analysts can run repeatable queries without manual UI steps.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.