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 provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

Auto-activating skill guiding metadata extractor implementations within data pipelines, covering ETL, transformation, orchestration, and streaming best practices.

Automates guidance for statistical significance calculations in data analytics, offering triggered best practices, example implementations in SQL, visualization, and BI workflows, plus validation against common standards.

This comprehensive guide details the implementation of Cloud Data Loss Prevention (DLP) using industry-leading tools such as Amazon Macie, Google Cloud DLP, and Azure Information Protection. It provides structured workflows for discovering, classifying, de-identifying, and protecting sensitive data (PII, PHI, financial data) across cloud storage, databases, and data pipelines. Essential for achieving compliance with global regulations (GDPR, HIPAA, PCI DSS) and integrating robust data governance into CI/CD processes.

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.

Transform vague research directions into concrete, problem-anchored, elegant, and implementation-ready method plans through iterative AI review. Ideal for refining research proposals, decomposing complex problems, and creating paper-worthy research methods that stay focused and current in the foundation-model era.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.

This comprehensive guide details the process of implementing a secure and robust system for handling Palantir Foundry webhook events. It covers key steps including API-based webhook registration, mandatory signature verification with replay protection, sophisticated event routing for various object lifecycles (create, update, delete), and ensuring idempotent processing using caching mechanisms like Redis. Use this when external microservices must react reliably and immediately to changes within the Foundry Ontology or data assets.

This skill converts vague business questions or monitoring needs into a complete, structured, and implementation-ready dashboard specification. It guides users to define key metrics (KPIs), chart types, axes, filters, and optimal layout, providing data engineers and BI developers with all necessary details to build the dashboard without ambiguity.

This guide provides rigorous methodology for authors to structure the policy and institutional background section of empirical economic manuscripts. Highlighting the journal's reliance on verifiable details (exact file names, dates, implementation rules), it teaches how to connect policy context directly to quasi-experimental identification strategies (like DID and event-study), ensuring the validity of causal claims and preventing common desk-reject issues.