Skill UI

A comprehensive guide detailing the architecture and strategy for migrating a design system from legacy platforms (like Figma or Adobe) to the Canva Connect API. It employs the Strangler Fig Pattern to ensure a safe, gradual transition, covering asset mapping, adapter layer implementation, and feature flag traffic splitting across multiple phases.

This skill runs a comprehensive, sequenced pipeline (Patterns 1-5) to structurally analyze a monolithic codebase. It inventories components, detects domain duplication, resolves hierarchy issues, measures inter-module coupling, and groups components into candidate, domain-aligned services. It is essential for architects planning service extraction, providing actionable metrics and DDD insights before refactoring or splitting the application.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

A detailed playbook covering CRLF (Carriage Return Line Feed) injection techniques. This exploit targets insecure handling of user input within HTTP response headers (e.g., Location, Set-Cookie) or log files. It demonstrates advanced attacks like response splitting, XSS escalation via double CRLF, cache poisoning, and session fixation, essential for deep security testing.

This comprehensive guide covers essential operations for handling PDF documents, including text, table, and metadata extraction using libraries like pdfplumber. It details document creation with reportlab, merging/splitting with pypdf/qpdf, and advanced OCR for scanned PDFs. Ideal for automating complex document workflows and structured data ingestion.

A robust toolkit for deterministic, structural PDF manipulation. This skill allows users to perform programmatic tasks such as extracting text and tables from reports, merging multiple PDF files, splitting documents by page ranges, filling out form fields, or generating entirely new PDFs from structured data (e.g., JSON). It is designed for explicit, byte-level operations, distinguishing it from LLM-based natural language rewriting tools.

A comprehensive style guide for implementing the Cyber Y2K aesthetic across web and mobile applications. This design package captures the retro-futuristic blend of late 90s/early 2000s digital culture, modern glitch art, and iridescent holographic gradients. It provides detailed code examples using CSS, SwiftUI, and Flutter to achieve key effects like RGB splitting, animated gradients, and aggressive vector typography.

A comprehensive guide detailing modern architectural patterns for building high-performance user interfaces with React and Next.js. Covers advanced techniques including Compound Components, Render Props, custom state hooks (like useQuery and useDebounce), advanced state management (Context + Reducer), and performance optimization strategies like memoization and code splitting.

This guide provides advanced methodological principles for conducting theoretically grounded heterogeneity analysis in financial research. It emphasizes that any dimension used for splitting the data (e.g., ownership, bank type, financial constraint) must be predicted by the underlying economic mechanism, rather than being an arbitrary data exploration. It details best practices, statistical rigor (group coefficient difference testing), and common pitfalls to avoid.

This advanced analytical skill focuses on rigorously identifying and testing the specific transmission channels through which external shocks affect an economy. Essential for high-impact international economics research, it moves beyond descriptive mechanism narratives by focusing on testable proxies across trade, investment, finance, and expectation channels. It requires advanced econometrics (e.g., mediation analysis, sample splitting) and careful identification strategy to ensure causal claims are robust.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This comprehensive audit checklist is designed for authors submitting papers to the ACM RecSys conference. It guides users through selecting the correct specialized track (Main, Industry, Reproducibility), adhering to strict page budgets, ensuring thorough double-blind anonymization, and mitigating common submission risks like identity leaks and improper data splitting.