Skill UI

Master modern Julia 1.10+ development with performance optimization, multiple dispatch, and production-ready practices. Covers scientific computing, data science, machine learning, web development, and package management. Focus on type stability, modern tooling, testing, and CI/CD pipelines for high-performance applications.

Instrument LLM applications with Langfuse tracing, prompt versioning and evaluation. Covers SDK setup, masking sensitive data, span verification, dataset comparison and debugging production LLM pipelines.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

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

A powerful tool utilizing Rube MCP to automate interactions with Make (formerly Integromat) scenarios. It allows users to retrieve detailed operation logs, monitor usage data, and validate critical configuration parameters such as supported languages and timezones. Ideal for building comprehensive workflow monitoring dashboards and ensuring the integrity of complex integration pipelines.

Administer, optimize, and secure MongoDB NoSQL databases in development and production. Covers installation, user setup, CRUD operations, indexing, aggregation pipelines, replica sets, and backup/restore.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

Secure OpenClaw deployments with preflight hardening checks, CI/CD guardrails, container runtime restrictions, and post-deploy verification. This skill provides a repeatable security workflow for build pipelines, container runtime lockdown, production promotion gates, data protection, and incident-ready rollback patterns.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.

Build and operate Retrieval-Augmented Generation (RAG) systems using vector stores, embedding pipelines, and hybrid search. This skill covers ingesting documents, generating embeddings, storing in vector databases, and serving grounded LLM responses. It includes setting up embedding pipelines, vector store infrastructure, and deploying hybrid search (dense + sparse) to reduce hallucinations and improve semantic search over large document collections.

This skill generates Robot Framework tests using keyword-driven syntax with Python. It supports SeleniumLibrary for web UI testing and RequestsLibrary for API testing. Users can define custom keywords and create data-driven test cases. Ideal for automation engineers needing structured test scripts for web applications and backend services. Supports cloud integration and CI/CD pipelines.