Skill UI

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

A comprehensive guide for developers on integrating and tuning with Veeva Vault using its REST API, VQL (Veeva Query Language), and VAPIL Java SDK. This skill covers authentication methods, common data patterns, document lifecycle management, and detailed error handling, making it essential for clinical operations and document management in life sciences.

A comprehensive guide to optimizing Webflow usage costs. This skill teaches advanced strategies for reducing API expenses by prioritizing CDN-cached reads (free and unlimited), implementing bulk endpoint operations, caching collection schemas, and replacing inefficient polling with Webhook-driven updates. It also provides methods for API usage monitoring and recommending the optimal site plan.

This guide provides detailed instructions for optimizing cost and implementing voice-to-text API integrations using Wispr Flow. It covers best practices for WebSocket streaming, REST API usage, context awareness (like programming code), and robust error handling, helping developers build advanced voice-powered applications.

This guide provides comprehensive technical specifications and cost tuning guidance for the Workhuman Social Recognition and rewards API. It covers core concepts such as peer-to-peer recognition flow, configurable award levels, and bidirectional synchronization with major HRIS systems (Workday, SAP SuccessFactors). Use this resource when building complex, integrated employee recognition and rewards workflows across enterprise platforms.

This domain-agnostic skill ranks impediments, risks, and gaps by using a specialized value-stream scoring model. It evaluates countermeasures based on four criteria: Return on Investment (ROI), Cost to Implement, Ease of Deployment, and Risk Factor. A proprietary formula calculates a final Priority score (1-100), allowing users to quickly determine the optimal sequence for remediation, improvement backlogs, or triage items.

This skill guides users through building comprehensive Smart City and IoT solutions on the Azure platform. It covers the entire solution lifecycle, from initial high-level requirements and defining city domains (e.g., traffic, energy) to designing robust, scalable architectures. Outputs include detailed reference architectures, security checklists, cost strategies, and phased implementation roadmaps for enterprise deployment.

This utility displays the precise, real-time token usage and estimated cost savings for the current session. It reads directly from the internal session log, ensuring objective and accurate data without requiring AI computation. Triggered by /caveman-stats, it provides immediate performance metrics to monitor API costs and usage patterns.

This skill fetches and compares live pricing for various Azure services, such as Virtual Machines, Storage, and SQL Databases. It utilizes the Azure Retail Prices API to allow users to check costs in specific geographic regions or compare the same service across multiple regions, helping them find the most cost-effective deployment option.

Use this skill to deeply inspect and audit the entire process of local AI coding-agent sessions. It tracks critical operational metrics such as token consumption, cost spikes, tool failures, latency anomalies, and overall health scores. Ideal for debugging unreliable code runs, establishing CI/CD health gates, or comparing different attempts to ensure semantic accuracy.