Skill UI

This guide details the setup and management of the Juicebox AI platform across multiple isolated environments: Development, Staging, and Production. It ensures workspace isolation, controls data access, and maintains data integrity by differentiating sample datasets (Dev), full datasets with restricted exports (Staging), and full-featured production environments. Follow this workflow to safely deploy and test analyses from development to live production.

This comprehensive workflow automates the core source-to-Lokalise process. Users can upload source translation files (JSON, XLIFF, PO, etc.), programmatically create new keys from code scans or CMS exports, update existing keys, and manage release tags. It is designed for CI/CD pipelines and bulk content synchronization.

Provides comprehensive patterns for data synchronization, ETL processes, and data migration specifically for MaintainX. Use this when needing to keep data consistent between MaintainX and external databases, data warehouses (like BigQuery), or when performing bulk data exports to formats like CSV. It covers incremental syncing, full exports, and data reconciliation checks.

Orchestrate PhantomBuster cloud agents through the Composio MCP integration, automating lead generation, web scraping, usage monitoring, container tracking, CSV exports, and hCaptcha solving in a single workflow.

Creates a Next.js page that renders App Store screenshots as marketing ads, exports Apple‑sized images via html-to-image, and supports branded styling, locale tabs, and reusable theme presets for iOS listings.

Build a PyMISP-driven threat intelligence platform that ingests multiple IOC feeds, tags MITRE ATT&CK techniques, enriches data via VirusTotal and AbuseIPDB, and exports STIX 2.1 bundles for SIEM pipelines.

Automates Coda document and data workflows through Rube MCP/Composio, covering searches, tables, formulas, exports, permissions, sharing, and publishing so bots can manage docs without manual API fiddling.

A comprehensive guide detailing three validated architectural patterns (Monolith, Service Layer, Microservice) for integrating with the Canva Connect API. This blueprint helps developers choose the optimal structure—from simple MVPs to complex enterprise systems—by addressing concerns like token management, asynchronous exports, rate limiting, and scaling strategies.

This guide provides advanced methods to significantly boost Clari API performance and data pipeline efficiency. It covers parallelizing multi-period data exports using Python's concurrency features, implementing a robust local caching mechanism with Time-To-Live (TTL), and optimizing data warehouse loading using SQL MERGE statements. Use this when facing slow exports, high API call volume, or requiring fast data synchronization in multi-forecast environments.

This skill provides robust mechanisms to interact with the Clari API, specifically designed to handle rate limiting (HTTP 429 errors) and manage asynchronous bulk export jobs. It implements exponential backoff and sequential scheduling, ensuring reliable data retrieval and forecasting exports without hitting API limits. Ideal for automated data pipelines and background job processing.

A production-ready guide detailing the best-practice architecture for integrating with the Canva Connect API. It outlines the project structure, layer architecture (Routes, Services, Client, Infrastructure), authentication flow (OAuth 2.0 PKCE), and best practices for handling design creation, asset management, and exports. Use this when building new Canva-powered applications or standardizing existing integration projects.

This comprehensive guide diagnoses and fixes common Clari API errors, including 401 Unauthorized, empty exports, job timeouts, and data mismatches. It provides step-by-step solutions for debugging data extraction and forecasting issues using API calls.