Skill UI

Guides building bespoke interactive data visualizations with d3.js across vanilla or framework-based JavaScript, covering custom layouts, transitions, and responsive sizing.

This guide provides a comprehensive framework for minimizing cloud expenditure on Databricks. It covers identifying cost drivers via system billing tables, implementing cluster policies, utilizing spot instances for batch processing, right-sizing SQL warehouses, and automating the shutdown of idle development clusters. Use it when managing cloud spending or optimizing data platform usage.

A comprehensive guide detailing optimization techniques for Databricks environments. It covers optimal cluster sizing for various workloads (ETL, ML, Streaming), advanced Spark configurations (AQE, memory management), and deep data optimization strategies for Delta Lake, including Z-Ordering and Liquid Clustering. It also provides methods for query plan analysis and join optimization.

This skill helps manage and optimize resource usage and API costs within Evernote integrations. It provides utilities for monitoring monthly upload quotas, compressing images, resizing large files, and performing storage cleanup to prevent exceeding account limits and ensuring efficient data handling.

Guide detailing Next.js 14+ App Router patterns, emphasizing Server Components, ISR caching, middleware, server actions, and parallel routes to build scalable, data-driven apps with minimal client JS.

This skill provides a comprehensive, expert guide to the entire reverse engineering lifecycle. It covers advanced techniques for binary analysis, including static analysis (disassembly, metadata extraction, control flow mapping) and dynamic analysis (API hooking, memory tracing). Mastery includes utilizing industry-leading tools such as IDA Pro, Ghidra, radare2, and specialized frameworks like pwntools and angr. The methodology is structured for secure research, CTF challenges, and vulnerability assessment, emphasizing best practices and ethical use.

This comprehensive toolkit guides senior UX designers and researchers through the entire product development lifecycle. It covers generating data-backed personas, mapping user journeys, planning structured usability tests, and synthesizing raw research data into actionable design recommendations. Use it when conducting deep user research or validating product designs.

This powerful tool facilitates comprehensive market research by extracting diverse data points from major consumer and business platforms, including Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor. It is designed to provide structured data for market sizing, analyzing regional demand, tracking pricing trends, and validating consumer behavior, enabling users to identify actionable business opportunities and potential risks.

Guides product managers through TAM, SAM, and SOM calculations with adaptive questions, data citations, and context-aware prompts to build credible, investor-ready market size estimates for product ideas.

This skill provides comprehensive methods for collecting and synthesizing Open-Source Intelligence (OSINT) regarding threat actors, malicious infrastructure, and attack campaigns. It utilizes public data sources, passive reconnaissance tools (like WHOIS, Certificate Transparency), and specialized platforms (Maltego, Shodan, SpiderFoot) to map adversary context and perform pre-engagement intelligence gathering for authorized red team assessments. Emphasis is placed on passive collection techniques to ensure legality and operational security.

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 skill provides a comprehensive guide to implementing high-performance, type-safe, in-memory caching in Go. It covers advanced features including nine eviction algorithms (e.g., W-TinyLFU, LRU), Time-To-Live (TTL), read-through loaders, and robust capacity sizing techniques. It is ideal for backend services requiring reliable, low-latency data access and efficient resource management.