Skill UI

This SDK provides a comprehensive Python interface for interacting with Azure Blob Storage, Microsoft's scalable object storage service. It enables users to perform core object storage operations, including uploading, downloading, listing, deleting blobs, managing containers, and setting custom metadata. The library supports both synchronous and asynchronous workflows, making it ideal for building robust data pipelines, data lakes, or cloud-native applications that require reliable, scalable file storage.

This is a comprehensive client library for interacting with Azure Blob Storage, Microsoft's scalable object storage solution for the cloud. It allows developers to perform core operations in Rust, such as uploading, downloading, listing, creating, and managing blobs and containers. Ideal for building cloud-native applications that require persistent, scalable data storage.

This SDK provides comprehensive Python support for Azure Data Lake Storage Gen2. It enables developers to interact with hierarchical file systems, performing essential big data operations such as creating, listing, uploading, downloading, and managing metadata for files and directories. Ideal for building data analytics pipelines and cloud-based data processing workflows.

A comprehensive Python SDK for interacting with Azure Queue Storage. This tool enables reliable, asynchronous communication patterns by managing message queuing, task distribution, and data processing workflows. It supports core operations including sending, receiving, peeking, updating, and deleting messages, making it ideal for building resilient, decoupled backend systems on Azure.

Automates Box cloud storage operations through Rube MCP, covering uploads, downloads, content search, folder lifecycle, sharing, collaborations, metadata queries, and sign requests; always start with RUBE_SEARCH_TOOLS to sync the current schema.

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

Guidelines for building Convex backends with TypeScript, detailing syntax, validators, schema design, queries, mutations, actions, scheduling, and file storage so you can implement Convex database operations, real-time updates, and cron jobs consistently.

This comprehensive guide details essential security best practices for integrating with Customer.io. It covers secure credential storage using secrets managers, sanitizing Personally Identifiable Information (PII) via hashing, verifying webhook signatures (HMAC-SHA256) to ensure data integrity, and establishing procedures for API key rotation to maintain compliance with GDPR/CCPA regulations.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

Provides best practices for managing the entire document lifecycle within Documenso integrations. Covers secure PII handling, implementing automated data retention policies (e.g., deleting old drafts and archiving completed documents), and fulfilling GDPR data subject access and erasure requests. Essential for maintaining compliance and secure document storage.

Automates Dropbox file, folder, sharing, upload/download, search and metadata workflows via the Rube MCP (Composio) toolkit while always querying the latest schemas before invoking storage tools.

This skill details best practices for handling and structuring Evernote data. It covers designing local database schemas (SQL), advanced ENML content processing (to Markdown/HTML), managing attachments, and implementing reliable incremental synchronization using Update Sequence Numbers (USN). Ideal for developers building robust applications requiring deep, local data access and integrity checks for Evernote content.