Skill UI

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

Provides comprehensive guidelines for securing CoreWeave cloud deployments on Kubernetes. Covers critical security areas including credential management, implementing Role-Based Access Control (RBAC), enforcing network isolation policies, and handling sensitive secrets for ML model access. Essential for hardening GPU workloads and protecting data in cloud environments.

Generates publication-quality, editable SVG vector graphics for structured diagrams, including system architectures, complex workflows, and pipelines. By taking a structured FigureSpec JSON as input, this tool ensures deterministic output: the same spec always yields the same diagram, making it ideal for formal documentation and system modeling where precise layout is mandatory. It operates entirely locally without external APIs. Use when you need a highly structured, technical diagram rather than a natural illustration or data plot.

This skill automatically converts structured data specifications (such as CSV, JSON schemas, SQL DDL, or Protobuf definitions) into functional, production-ready code. It generates complete models, validation logic, full CRUD API endpoints, and unit tests in multiple languages (Python, TypeScript, Go, etc.), significantly reducing boilerplate coding time and ensuring type safety.

This skill converts complex technical descriptions, such as system architectures, data flow diagrams, flowcharts, and sequence diagrams, into professional, production-quality SVG diagrams. It supports various technical models, including Agent and Memory architectures, and exports the final output as high-resolution PNG files for seamless documentation and presentation use.

This comprehensive 'Hello World' script provides a hands-on guide for integrating with the Intercom API. It demonstrates the creation, searching, and management of core entities like Contacts, Conversations, and Messages. Ideal for developers starting a new Intercom integration, testing basic functionalities, or understanding the foundational Intercom data model using TypeScript.

This tool systematically converts a project roadmap into actionable specifications. It first analyzes features by grouping them into dependency waves. It then automatically dispatches sub-agents to generate complete feature specs (requirements, design, tasks, and JSON) for each wave. The core value lies in its comprehensive cross-spec review, which verifies data model consistency, interface alignment, and architectural integrity across the entire project, ensuring a cohesive final product.

A comprehensive guide and toolkit for developers managing MindTickle integrations. This resource details critical API schema changes, such as evolving course progress structures, supporting new quiz types, and updating SCIM/CRM provisioning models. It is essential for auditing, migrating, and ensuring the stability of data synchronization with external systems like Salesforce.

This guide provides a comprehensive workflow for building robust ETL data pipelines within Palantir Foundry. It teaches how to utilize Python transforms, including the @transform_df decorator, for dataset-to-dataset processing. Key topics covered include multi-input joins, implementing incremental updates for efficiency, low-level file I/O, and configuring Spark resources for heavy computations. Essential for data engineers working with complex data modeling in Foundry.

This guide details how to manage and reduce the high rate-limit costs associated with the SalesLoft API. It explains the cost model, emphasizing that deep pagination significantly increases consumption. Learn advanced strategies like incremental syncing, caching frequently accessed data, implementing webhooks, and request consolidation to maintain efficiency and stay within budget limits.

Provides comprehensive strategies for minimizing API credit consumption when using SerpApi. Techniques include implementing aggressive caching with Redis, utilizing the free Archive API for historical data retrieval, and selecting the appropriate engine (like Google Light API) to balance cost and speed. Includes cost calculation models and usage monitoring scripts.

This guide details how to use Shopify Metafields and Metaobjects via the GraphQL Admin API. Metafields allow attaching key-value custom data to existing resources (like products or orders), while Metaobjects enable defining entirely new, structured content types. Together, they provide a powerful way for developers to model complex custom data, often eliminating the need for external databases to extend e-commerce functionality.