Skill UI

Use the Parallel CLI for web search, URL/PDF extraction, deep research, data enrichment, entity discovery, and recurring web monitoring. Best when requests need current web evidence, academic sources, or change tracking.

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.

Provides comprehensive strategies for migrating embedding models within Qdrant. This guide covers zero-downtime alias swapping, side-by-side A/B testing, and transitioning from dense to hybrid search. It is essential for production environments requiring continuous availability when updating models, dimensions, or switching providers.

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.

This comprehensive guide details advanced strategies for managing time-series data and data retention in vector databases (like Qdrant). It is designed for scenarios where only recent data is relevant (e.g., social media feeds, news articles). Three core methods are presented: Shard Rotation (recommended for natural time boundaries), Collection Rotation (for per-period configuration needs), and Filter-and-Delete (for simple cleanup). The guide also covers hot/cold tiering to optimize performance.

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.

This advanced skill automates the entire lifecycle of cloud infrastructure deployment. Instead of manual documentation review, it researches current best practices across multi-cloud platforms (AWS, GCP, Azure), synthesizes a coherent deployment strategy, and generates production-grade Infrastructure as Code (IaC). It outputs comprehensive artifacts, including Terraform modules, Kubernetes manifests, CI/CD pipelines, monitoring setups, and cost estimates, ensuring secure and optimized deployment.

A comprehensive tool for searching the Model Context Protocol (MCP) across various GitHub locations, including PRs, issues, and discussions. It allows users to track the full lifecycle of protocol specifications, from initial proposals to final merged changes. Furthermore, it facilitates deep dives into pull request reviews and comments, enabling the extraction of key maintainer rationales and design intent for accurate technical understanding.

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.

This guide provides a comprehensive reference for common errors encountered when using the SerpApi. It covers issues like invalid API keys, exhausted search credits, missing parameters, and CAPTCHA blocks. Use this when your automated search results are incorrect, empty, or return unexpected status codes, allowing you to quickly diagnose the root cause and implement fixes.

This bundle provides comprehensive diagnostics for SerpApi, allowing users to collect critical data points such as account status, current credit usage, rate limits, and recent search history. It is indispensable for troubleshooting API integration issues, verifying account health, or compiling detailed information for technical support tickets.

This guide details how to deploy SerpApi-powered search functionality into production environments using modern serverless platforms like Vercel and Google Cloud Run. It critically emphasizes the security requirement: all API calls must be proxied through a secure backend endpoint and must never expose the API key to client-side browsers, ensuring robust and secure search integrations.