Skill UI

This guide details how to optimize the operational costs associated with Persona's identity verification services. It covers best practices like selecting appropriate verification templates, implementing robust caching mechanisms, and structuring API calls to prevent duplicate inquiries, ensuring efficient and cost-effective KYC workflows.

Optimizes the performance of the Persona API for identity verification workflows. This guide demonstrates best practices such as implementing batch inquiry creation, caching verification results, and managing parallel polling to ensure efficient and reliable data retrieval when dealing with large volumes of identity data.

This skill provides comprehensive integration examples for the Podium API, a platform for business messaging and communication. It demonstrates core functionalities, including listing all business locations, retrieving specific customer contacts for a site, and sending test messages directly through the Podium platform. It is ideal for automating communication workflows, managing customer interactions, or integrating messaging and review features into existing business systems.

This skill outlines implementation patterns for integrating with the Podium REST API, focusing specifically on handling rate limits and OAuth2 authentication. It provides best practices for secure API calls across key business functionalities such as messaging, review management, and payments. Learn how to implement error handling (e.g., 429 Rate Limited) and robust retry logic for stable system operation.

This skill acts as a Senior Solution Architect specializing in the Microsoft Power Platform ecosystem (Power Apps, Power Automate, Power BI, etc.). It excels at analyzing unstructured input, such as meeting transcripts or high-level use case descriptions, and generating a comprehensive technical solution architecture. The output includes component selection, detailed implementation steps, and visualizations (e.g., Mermaid.js diagrams) required to build a robust digital solution.

This skill provides a comprehensive workflow for managing Requests for Information (RFIs) within the Procore platform. It enables users to programmatically create, assign, update responses, track status changes, and officially close RFIs using the Procore API. Ideal for automating critical steps in construction project management.

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 runbook provides implementation patterns for interacting with the Procore REST API using OAuth2 authentication. It guides users on accessing company data, handling common API errors (e.g., 401, 429, 403), and executing core project management workflows within the construction industry.

This guide details how to establish secure API access to the Procore construction management platform. It utilizes the OAuth2 client credentials flow, allowing developers to obtain necessary access tokens using client IDs and secrets. This setup is crucial for integrating custom applications to manage projects, retrieve RFIs, or handle submittals programmatically.

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.

A comprehensive guide for diagnosing and reducing high memory consumption in Qdrant. This resource is crucial when dealing with reports of 'memory leak', 'out of memory', or excessive RAM growth. It details how Qdrant manages resident memory and OS page cache, and provides advanced techniques such as quantization, using float16/int8 data types, and strategically storing components on disk (like HNSW indexes or sparse vectors) to minimize the memory footprint and ensure stable, scalable operation.

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.