Skill UI

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Enables Claude to analyze request and data throughput, detect bottlenecks, and suggest scaling strategies via the throughput-analyzer plugin when performance or capacity optimization is requested.

This SDK provides comprehensive management plane capabilities for provisioning and controlling Microsoft Fabric compute capacities via Azure Resource Manager (ARM). Use it to perform core lifecycle operations such as creating, updating (including scaling), suspending, resuming, and deleting Fabric capacities. Note: This SDK manages the underlying compute resource, not the data plane elements like workspaces or lakehouses.

This SDK provides comprehensive management plane capabilities for provisioning and controlling Microsoft Fabric compute capacities via Azure Resource Manager (ARM). Use it to perform core lifecycle operations such as creating, updating (including scaling), suspending, resuming, and deleting Fabric capacities. Note: This SDK manages the underlying compute resource, not the data plane elements like workspaces or lakehouses.

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

A comprehensive guide and code implementation for scaling data enrichment pipelines to handle high volumes (10K-100K+ leads monthly). It covers capacity planning, optimizing batch processing using queues (e.g., BullMQ/Redis), managing rate limits, and designing multi-table architectures for robust, large-scale data ingestion.

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.

This skill details advanced techniques for enhancing the effective context capacity of LLMs. It outlines four core strategies—Compaction, Observation Masking, KV-Cache Optimization, and Context Partitioning—to prevent context limits from constraining task complexity. By strategically managing token usage, users can reduce operational costs, minimize latency, and implement robust, long-running agent systems for handling large documents or conversations.

A comprehensive guide and toolkit for performing load testing, capacity planning, and implementing scaling strategies for Exa API integrations. It covers best practices for managing rate limits (QPS), using tools like k6, implementing caching (LRU), and optimizing search architectures for high-throughput, low-latency applications, such as RAG systems.

This skill provides advanced methods for load testing and scaling web scraping pipelines using Firecrawl. It manages rate limits and optimizes throughput by implementing concurrency control, batch processing, and queue-based request throttling. Essential for capacity planning, stress-testing scraping performance, or maximizing data ingestion efficiency across various service plans.