Skill UI

This tool facilitates comprehensive prior art searches across global patent databases (Google Patents, Espacenet) and academic literature (Google Scholar, arXiv). It guides users through extracting core inventive concepts, performing targeted searches using keywords and IPC/CPC codes, and analyzing the results to assess novelty, overlap risk, and general freedom-to-operate.

This skill scans live websites or local codebases to detect all data collection signals, including cookies, analytics, forms, and third-party processors. It then generates a comprehensive, 12-section privacy policy tailored for GDPR and CCPA compliance. The system maps detected data touchpoints to specific legal bases (Consent, Contract, etc.), ensuring the resulting policy accurately reflects the actual data practices of your application or website.

This skill automates the critical submittal workflow within the Procore construction management platform. Users can programmatically create new submittals, defining necessary specifications, assigning reviewers, and setting due dates. It further supports updating the submittal status (e.g., approving or rejecting) and listing filtered results, ensuring systematic tracking and smooth management of the entire project review cycle.

This guide provides comprehensive guidance on scaling Qdrant, a vector database. It helps users diagnose performance bottlenecks and determine the optimal horizontal or vertical scaling strategy. Strategies are categorized based on specific needs: increasing data volume, improving query throughput (QPS), minimizing query latency, or managing high query result volumes.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

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.

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This skill provides implementation patterns for interacting with RunwayML webhooks. It enables complex tasks such as AI video generation, image editing, and advanced creative content workflows. By using the provided Python SDK pattern, developers can trigger video creation tasks (e.g., image-to-video) and handle the asynchronous results, making it ideal for integrating high-end creative AI into applications.

Master advanced techniques for optimizing Salesforce API interactions. This guide covers optimizing SOQL queries using selective fields and indexed WHERE clauses. It details methods to minimize API calls by utilizing Collections and Composite APIs, implementing metadata caching, and efficiently streaming large result sets via Bulk API 2.0, ensuring stable and high-performance integrations.

Provides production-ready, robust patterns for interacting with the SalesLoft REST API v2. This pattern solves common integration challenges by offering a type-safe client singleton, automatic iteration over paginated results, and intelligent handling of rate-limiting errors (429). Ideal for building scalable, reliable SalesLoft integrations using TypeScript or Python.

Automates the entire process of web research and professional communication. It takes raw web search results and formats them into polished, structured messages, digests, or threaded summaries optimized for Slack. Users can generate outputs in Slack's native Markdown format, Block Kit JSON, or a ready-to-use Webhook command, perfect for weekly reports, security alerts, and competitive intelligence updates.

This guide provides a comprehensive setup for Continuous Integration (CI) and Continuous Deployment (CD) for SerpApi integrations. It outlines using fixture-based unit tests via GitHub Actions for Pull Requests (preventing API credit consumption) and controlled live integration tests on the main branch. This ensures robust and reliable search result parsing validation.