Skill UI

Master the implementation of Firecrawl webhooks for robust, real-time handling of asynchronous crawling and batch scraping jobs. Instead of inefficiently polling the crawl status, configure webhook endpoints to receive instant POST events (e.g., `crawl.page`, `crawl.completed`). This guide covers event types, signature verification using HMAC-SHA256, and processing streamed data for building advanced data pipelines.

This skill guides you through integrating PostHog's Customer Data Platform (CDP) features. It covers setting up webhooks to trigger real-time actions (e.g., syncing to CRM, sending notifications) when specific user events occur. Furthermore, it demonstrates how to query event data using the Events API and perform complex funnel analysis using HogQL, allowing for deep, custom insights into user behavior.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It is designed for modeling complex systems where entities interact with shared resources and time-based events occur, such as manufacturing lines, service queues, network traffic, or logistics networks. Use this skill to analyze resource contention, simulate process optimization, conduct capacity planning, and model dynamic system behaviors.

Timesketch is a collaborative, open-source platform for security teams to analyze and reconstruct complex forensic timelines. It ingests and normalizes multi-source security data (logs, artifacts) using methods like Plaso, CSV, and JSONL. Users can visualize event chronology, track attack chains, and utilize advanced built-in analyzers (e.g., Sigma, Chain of Events) for comprehensive incident investigation and documentation.

This skill provides structured procedures for detecting Golden Ticket attacks, a severe threat involving forging Kerberos Tickets in Active Directory. It analyzes Windows Event ID 4769 for indicators such as RC4 encryption downgrades, abnormal ticket lifetimes, and inconsistencies in TGS/TGT requests. Designed for SOC analysts and threat hunters using SIEM platforms (Splunk/Elastic) to enhance security monitoring coverage against credential theft.

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.

scikit-survival is a comprehensive Python library built on scikit-learn, designed for rigorous time-to-event analysis. It specializes in handling censored data (right, left, or interval-censored) and provides multiple advanced modeling techniques, including Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs. Users can evaluate model performance using metrics like the Concordance Index and Brier Score, making it ideal for clinical, reliability, and survival research.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It enables users to model complex systems involving processes, shared resources, queues, and time-based interactions. It is ideal for analyzing manufacturing lines, network traffic, logistics, or service operations where resource contention and irregular, time-based events are key factors.

This skill provides a robust solution for monitoring critical forecast changes within Clari. Since Clari lacks native real-time webhooks for all data, this system implements change detection by polling periodic data exports and comparing current snapshots against historical records. It calculates the percentage change and can trigger immediate alerts via platforms like Slack, ensuring users are always aware of significant shifts in revenue forecasts or sales pipelines.

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.

This skill provides comprehensive intelligence across finance and media. Users can search real-time news, analyze media bias across multiple dimensions, get live stock/crypto data with AI forecasts, price financial options using ML models, and synthesize balanced perspectives on current events. It is ideal for deep research, investment analysis, and media literacy.