Skill UI

This skill provides comprehensive strategies for implementing robust caching mechanisms for OpenRouter API calls. It covers in-memory caching using Python, persistent caching with Redis for scalability, and utilizing provider-specific features like Anthropic prompt caching. By implementing deterministic key generation and managing Time-To-Live (TTL), developers can significantly reduce API costs and improve application latency when handling repetitive or similar LLM queries, making it ideal for RAG systems and high-volume applications.

Flash Attention optimizer delivers 2-4x speedup and 10-20x memory savings for transformer attention, ideal when training or running long-context models that hit GPU memory or latency limits; supports PyTorch native scaled_dot_product_attention, the flash-attn library, H100 FP8 precision, and sliding-window attention.

Helps analyze and enhance application caching strategies by reviewing TTLs, key structures, hit rates, and invalidation policies to boost Redis or Memcached performance.

Automate Optimoroute workflows through Composio’s Rube MCP by always discovering tool schemas before executing `RUBE_MANAGE_CONNECTIONS`, `RUBE_MULTI_EXECUTE_TOOL`, or other toolkit calls, ensuring session reuse and schema compliance.

Automate Owl Protocol operations through Composio’s toolkit on Rube MCP: search tools for current schemas, manage owl_protocol connections, then execute RUBE_MULTI_EXECUTE_TOOL workflows with schema-compliant data and memory placeholders.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

A comprehensive guide covering the entire web performance optimization lifecycle. Learn how to measure Core Web Vitals (LCP, INP, CLS), identify bottlenecks using specialized tools like Lighthouse and DevTools, and apply advanced techniques such as code splitting, lazy loading, and memory leak fixes to ensure fast, stable, and high-quality user experiences.

Automates load, stress, spike, and endurance tests through the performance-test-suite plugin, collecting response time, throughput, and error-rate metrics to spot CPU/memory/database/network bottlenecks and deliver graphs plus optimization guidance.

Automates Pilvio operations via Composio’s Rube MCP by discovering the latest tool schemas, validating Pilvio connections, and invoking RUBE_MULTI_EXECUTE_TOOL with memory/session management; covers workflows, pagination, and schema compliance tips.

This skill implements a persistent, file-based planning workflow, acting as an external 'working memory' for AI coding agents. By maintaining key documents like task_plan.md, findings.md, and progress.md directly in the project directory, it ensures that complex, multi-step projects, research tasks, and long sessions survive context loss and interruptions. It supports session recovery and structured project management.

Automates Postgrid operations via Composio’s toolkit over Rube MCP by laying out the connection, discovery, execution, and schema-compliance steps so agents always search tools first, verify the active Postgrid link, and run workflows with correct memory and session handling.