Skill UI

Expert patterns for building Azure Functions: isolated worker model, Durable Functions orchestration, cold start optimization, and production-ready serverless design across .NET, Python, and Node.js.

Bilig WorkPaper provides a code-first runtime environment for complex spreadsheet business logic. It allows agents to interact with structured data using formulas and JSON workbooks via deterministic API calls (MCP/TypeScript). This is ideal for replacing unreliable UI automation in use cases like quote calculators, budget modeling, and data validation, ensuring calculation integrity through explicit read-back proof.

This skill guides you in implementing Command Query Responsibility Segregation (CQRS) patterns for scalable software architectures. It focuses on separating read and write models to optimize query performance and support event-sourced systems. Use this when you need to scale reads independently from writes or handle complex query scenarios. It is suitable for building high-performance reporting systems where detailed read/write data models are required.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

Provides read-only platform APIs for inspecting model management, pricing, and usage tracking. Use to verify account identity, region, endpoints, time windows, costs, and gaps without modifying keys or billing.

Master Firebase backend services including Authentication, Firestore, Realtime Database, Cloud Functions, Storage, and Hosting. Learn security rules, data modeling for read-heavy workloads, and cost optimization to avoid pitfalls like expensive reads and query limitations.

This skill evaluates content for AI citability based on research from Princeton and Georgia Tech. It scores content on answer block quality, self-containment, and structural readability to optimize visibility in AI-generated responses. Ideal for SEO and GEO strategies to ensure AI models extract and cite your content effectively.

This skill enables users to add and manage structured evaluation results within Hugging Face model cards. It supports extracting evaluation tables from README files, importing benchmark scores via the Artificial Analysis API, and executing custom model evaluations using vLLM or lighteval backends. It ensures compatibility with the model-index metadata format for standardized model releases.

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.

Delegate bounded coding tasks to the Oh My Pi (omp) CLI implementer, then review and land the resulting diff. Orchestrator writes the brief, relays it, and verifies output. Supports model selection, thinking levels, and read-only runs.