Skill UI

An internal utility designed for deep analysis of Python tracebacks. It performs Python-specific root-cause checks, provides guidance for minimal reproducible examples using pytest, and identifies precise targets for implementing defensive coding practices (like guard clauses or schema validation). Ideal for advanced debugging scenarios.

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

This skill transforms raw API specifications, Postman collections, or verbal descriptions into clean, developer-facing documentation. It produces comprehensive guides including detailed endpoints, parameters, request/response schemas, standardized error codes, and runnable code examples, making it ideal for developer portals or README files.

This skill generates a comprehensive, safe, and zero-downtime database migration plan for schema changes. It utilizes the industry-standard expand/contract pattern to ensure backward compatibility, detailing multiple phased steps (Expand, Backfill, Contract), necessary SQL scripts, robust rollback procedures, and detailed deployment runbooks. Essential for maintaining application availability during large-scale database updates.

This skill generates comprehensive, production-ready database schema documents. It covers Entity-Relationship (ER) diagrams, detailed SQL DDL definitions, constraint analysis, index strategies, and access pattern optimization. Use it when designing a new database, documenting an existing one, or modeling complex data relationships for engineering review.

This skill enforces a rigorous data validation protocol by establishing hard boundaries at every point data enters the system (API requests, user input, environment variables, webhooks, etc.). It mandates a four-phase process: detection, trust classification, mandatory filtering (schema validation, sanitization, type checking), and final verification. This prevents untrusted data from reaching core business logic, mitigating risks like SQL injection and XSS.

This skill helps diagnose, repair, and prevent complex Drizzle Kit database migration conflicts. It addresses issues arising from merging branches, rebasing, or PR updates involving generated SQL, snapshots, and journals. Use it to ensure stable schema evolution in multi-developer teams and harden CI/CD workflows.

This comprehensive skill demonstrates advanced usage patterns for Formik, a popular React form handling library. It covers basic form setup, complex validation schema creation using Yup (including conditional logic), field helper utilities, and integrating form submissions with GraphQL mutations for both creation and editing scenarios.

This guide details best practices for implementing robust, type-safe GraphQL queries and mutations. It covers using separate .gql files, running code generation tools, and implementing mandatory error handling (onError) and loading states. Use this when building complex, reliable data fetching layers with Apollo Client.

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.

This skill serves as the central configuration manager for AI agent projects, defining the single source-of-truth for all capabilities (skills, tools, servers, rules, plugins). It guides users through the entire system lifecycle using the `capa` CLI, covering installation, schema definition, secret management, and advanced troubleshooting. Use this skill whenever setting up, modifying, or troubleshooting project capabilities.