Skill UI

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.

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.

This skill provides comprehensive code review capabilities for analyzing git diffs or specific file scopes. It goes beyond simple syntax checks, assessing code for reuse opportunities, quality issues (e.g., redundant state, leaky abstractions), efficiency bottlenecks, and adherence to standards. It utilizes multiple specialized sub-agents (Reuse, Quality, Efficiency, Clarity) to perform parallel, read-only audits. Optionally, it can apply high-confidence, behavior-preserving fixes upon user request.

A meta-skill designed to capture hard-won, non-obvious knowledge—such as complex debugging steps, multi-step operational workflows (e.g., deployment, migrations), or project-specific facts—and convert them into reusable Agent Skills. It ensures that future sessions, regardless of the user or team member, start with proven procedures, preventing the need to rediscover solutions from scratch.

This agent analyzes a Monte Carlo monitor's report and configuration to identify alert patterns. It recommends concrete configuration changes for metric, custom SQL, validation, and table monitors, helping reduce false positives and alert fatigue without sacrificing real signal.

This skill functions as an optional agent companion for MDPR, a deterministic Markdown-to-presentation runtime. It helps review presentation workflows by providing semantic hints, visual evidence reports, and high-level layout intentions. Crucially, it keeps agent output evidence-based while letting MDPR maintain control over final slide geometry, rendering details, and layout coordinates.