Skill UI

Use Composio’s Airtable toolkit through Rube MCP to automate creating, querying, and managing records, fields, schemas, and comments while following the required tool discovery, pagination, and limit-handling steps.

Guides creation of algorithmic art philosophies and p5.js implementations that emphasize emergent behavior, seeded randomness, and a meticulously crafted algorithmic process, producing philosophy .md plus interactive .html/.js viewers for flow fields, particles, and controlled chaos.

Guides creation of algorithmic art philosophies and p5.js implementations that emphasize emergent behavior, seeded randomness, and a meticulously crafted algorithmic process, producing philosophy .md plus interactive .html/.js viewers for flow fields, particles, and controlled chaos.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

Provides comprehensive tools for managing enriched lead data in compliance with global privacy regulations like GDPR and CCPA. This skill guides practitioners through data classification (identifying PII sensitivity), input validation, deduplication, implementing data retention metadata, and anonymizing sensitive fields for secure reporting and analytics.

This comprehensive guide details how to migrate from multiple standalone data enrichment providers (like ZoomInfo, Apollo, Clearbit) or complex custom scripts into a unified Clay pipeline. It covers auditing current data stacks, mapping disparate fields, running parallel validation tests, and configuring Clay to consolidate data sources, significantly reducing complexity and operational costs.

This workflow guides the complete process of generating legal documents using the Documenso API. It covers creating document shells, uploading PDFs, defining multiple recipient roles (Signer, Approver, Viewer), setting specific signing orders, and strategically placing various interactive fields (Signature, Date, Text) across multiple pages. It is ideal for building complex, multi-party agreement workflows and managing the entire document lifecycle.

Implement comprehensive, template-based workflows for documents. This skill allows users to create reusable templates, programmatically generate multiple instances of documents by filling pre-defined fields, and manage complex signing processes. It supports direct signing links for public access and provides React components for seamless embedding into custom applications.

This resource provides a minimal working example for implementing a complete digital document signing workflow using the Documenso SDK. It comprehensively guides users through creating a document, uploading a PDF file, adding designated recipients, placing specific signature fields, and finally triggering the signing process via code examples (TypeScript, Python) or REST API calls. Use this when integrating document signing into new applications or testing core document management capabilities.

FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search and clustering of dense vectors. It supports indexing and searching on billions of vectors at scale, featuring GPU acceleration and multiple index types (Flat, IVF, HNSW, PQ) to balance speed and accuracy. It is essential for building high-performance, large-scale applications requiring fast k-NN retrieval or pure vector similarity matching, especially in fields like RAG and recommendation systems.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.