Skill UI

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.

This skill helps diagnose performance bottlenecks, slow jobs, and expensive queries across various data platforms (Airflow, dbt, Databricks). It uses a tiered investigation approach, starting with broad discovery to pinpoint problematic jobs or queries, bridging to affected tables, and finally drilling down to identify root causes, latency trends, and change-related regressions.

This skill guides users through the complete lifecycle of data quality incident management using Monte Carlo MCP tools. It performs detailed investigation, including root cause analysis and blast radius assessment. It then coordinates various external tools (APIs, CLI) to propose and execute appropriate remediation actions, ensuring all data incidents are resolved with full context.

This skill analyzes your data warehouse to pinpoint stale, unused, or redundant tables that contribute to unnecessary storage costs. It classifies waste patterns (e.g., Zombie, Temporary) and computes safety tiers, helping users understand which assets can safely be dropped or archived to optimize spending and improve data governance.

This advanced skill performs code review by simulating the documented engineering philosophies of historical figures (e.g., Linus Torvalds, Steve Jobs, Ken Thompson). It moves beyond generic AI findings by grounding critiques in specific, citable principles—such as 'good taste' or 'customer experience.' Ideal for high-stakes PRs, architectural reviews, and ensuring deep, pre-submission code hardening.

The Neon AI Gateway provides a single API endpoint and one credential to access a diverse catalog of frontier and open-source LLMs (including OpenAI, Anthropic, Google Gemini, Meta, Alibaba, and DeepSeek). This unified layer simplifies integration by eliminating the need for separate provider accounts and managing model-specific SDK complexities. It allows developers to switch between models seamlessly by changing only the model name, while also providing crucial branch-scoped isolation for development and CI environments.

Neon Functions provide long-running, stateful, serverless HTTP handlers deployed directly next to your Neon database branch. They are ideal for complex backend workloads that exceed traditional serverless limits, such as real-time WebSockets, Server-Sent Events (SSE) streaming, or sophisticated AI agents. Functions automatically inject the database connection URL and maintain state within the function scope, ensuring high performance and data proximity.

Neon Object Storage provides S3-compatible, branch-aware object storage directly integrated with your Neon Postgres project. It ensures that files (images, documents, user uploads) remain perfectly synchronized with the database schema across all development branches (dev, preview, staging, production). Use this skill when you need a single backend for both database rows and file assets, eliminating the need for separate cloud storage providers.

This skill provides comprehensive guidance on choosing and creating the appropriate Neon Postgres branch for development and testing workflows. Users must decide between a 'Normal' branch (for realistic migration testing with live data) or a 'Schema-Only' branch (for highly sensitive data environments). It details the decision rules, tool selection (CLI/MCP), and procedures for branch creation and resetting.

Generate comprehensive, ready-to-use CI/CD pipeline configurations for running automated API tests using Newman. This skill provides templates for major platforms like GitHub Actions, GitLab CI, Jenkins, and Azure DevOps. It ensures seamless integration of Postman collections into automated build processes, including setting up environment variables, running tests, and publishing detailed JUnit and HTML reports.

This skill is designed for complex, multi-step tasks requiring coordination among multiple AI agents. It implements a robust workflow engine that first generates a detailed plan (task graph, topology) and then orchestrates parallel execution with adversarial verification. Use this when the task spans many files, requires multiple specialized roles, or involves an overall 'workflow' concept, ensuring structured, reliable, and auditable results.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.