Skill UI

Master building high-performance asynchronous APIs using FastAPI, SQLAlchemy 2.0, and Pydantic V2. Covers microservices, WebSockets, security, testing, and deployment strategies for production-ready Python backend systems. Includes async patterns, database optimization, and observability.

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 investigates data incidents like freshness delays, volume anomalies, and ETL failures using Monte Carlo MCP tools. It combines observability metadata with optional direct data querying to identify root causes systematically.

This skill evaluates the health of data assets using Monte Carlo's observability platform. It generates structured reports on freshness, alerts, monitoring coverage, importance, and upstream dependencies. Use it when users inquire about asset status, reliability, or quality.

Routes ambiguous data-observability requests to the appropriate Monte Carlo skill or workflow. Activates reactively for unclear alerts, incidents, coverage gaps, or general data quality issues, asking clarifying questions first to avoid premature tool calls.

Analyzes data coverage gaps and creates monitors for warehouse tables and AI agents. Covers use-case analysis, data monitor creation, monitors-as-code YAML, and agent observability.

Diagnoses data pipeline performance issues such as slow jobs, expensive queries, and latency trends using Monte Carlo's cross-platform observability. Works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.

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.