Skill UI

Builds a robust, automated workflow to export critical sales data—including forecasts, quota details, adjustments, and CRM totals—from Clari CRM to a designated data warehouse. The pipeline supports multiple targets like Snowflake, BigQuery, and PostgreSQL, ensuring that real-time revenue intelligence is available for advanced analytics and business reporting.

This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.

This skill outlines how to implement and manage Role-Based Access Control (RBAC) and namespace isolation for multi-team GPU workloads running on CoreWeave's Kubernetes platform. It provides guidance on setting strict resource quotas, preventing noisy-neighbor issues, and defining granular roles (e.g., ML Engineer, Inference Operator). This is essential for maintaining resource isolation, ensuring compliance (SOC 2, HIPAA), and managing access across large-scale cloud deployments.

This guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

This skill provides methods for managing GPU quota limits and rate restrictions for CoreWeave cloud services. It includes using `kubectl` to check resource quotas and demonstrates implementing robust request queuing mechanisms using Python's `asyncio` library, ensuring stable and efficient resource allocation for high-concurrency inference endpoints.

A comprehensive guide to setting up CI/CD pipelines for ElevenLabs integrations. It implements a robust two-tier testing strategy: running mocked unit tests on every push to save API quota, and executing gated, real integration tests only on main branches or manually. Essential for automated validation of TTS projects using GitHub Actions.

A comprehensive diagnostic guide for common ElevenLabs API errors, organized by HTTP status code. It provides detailed causes, fixes, and troubleshooting steps for issues such as invalid API keys (401), quota exhaustion, exceeding character limits (400), and rate limiting (429). Essential for developers debugging TTS, voice cloning, or streaming requests.

This skill provides methods to optimize ElevenLabs usage costs for Text-to-Speech (TTS) workloads. It covers model selection (e.g., using Flash/Turbo for savings), implementing character-efficient text pre-processing to reduce character count, and setting up real-time quota monitoring with budget alerts. Ideal for developers managing high-volume, cost-sensitive voice applications.

This bundle collects comprehensive diagnostic evidence for ElevenLabs services. It automatically gathers SDK versions, API connectivity status, quota usage, voice inventories, and available model metadata. Use this tool when encountering persistent bugs or preparing comprehensive support tickets for technical support.

This guide sets up a professional, cost-effective local development workflow for ElevenLabs TTS/voice projects. It includes SDK mocking to prevent character quota overruns during testing, hot-reloading capabilities, and quota-aware scripts. Ideal for developers building robust AI voice applications.

A comprehensive guide to production-ready patterns for ElevenLabs' TypeScript and Python SDKs. This resource covers crucial best practices including singleton client implementation, type-safe TTS service wrappers, robust error classification (rate limiting, quota), and concurrency management using request queues. Essential for building scalable and reliable voice AI applications.

This skill implements a comprehensive solution for managing Miro REST API rate limits. It incorporates a credit-based monitoring system, exponential backoff with jitter, and request queuing. This ensures reliable, high-throughput data integration by respecting Miro's usage quotas and handling API throttling (429 errors) gracefully.