Skill UI

A comprehensive skill providing patterns for implementing data privacy, PII redaction, and robust audit logging when interacting with large language models like Claude. Use this when your application handles sensitive user data, requiring compliance with regulations such as GDPR or CCPA, ensuring all API calls are secure and auditable.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

This skill manages the complex process of upgrading AppFolio API integrations. It automatically detects current and latest API versions, handling breaking changes such as renamed fields, altered pagination models, and deprecated endpoints. The tool provides schema migration transforms and implements a robust rollback strategy, ensuring stable data synchronization during major platform updates.

Learn how to build complex, multi-step AI agents using Cohere's Chat API v2. This skill demonstrates defining external tools (like weather APIs or database searches) and implementing the full agent loop: model calls tool -> execute tool -> feed result back -> final answer. Ideal for creating autonomous, tool-using workflows that interact with external systems.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).

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.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

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.

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.

Finta is a comprehensive workflow tool designed for managing investor relations post-fundraise. It streamlines the process of sending periodic, data-rich updates (including MRR, ARR, and burn rate) to all investors. Users can track complex cap table changes, model future dilution, and leverage a network feature to find warm introductions, ensuring professional and compliant communication with Limited Partners (LPs).