1. Software Development

Implements JSON:API compliant REST endpoints for Ash resources with features like filtering, sorting, pagination, and includes.

Provides critical guidelines for Phoenix HTML and HEEx templates to prevent common bugs and syntax errors in form handling, conditionals, class attributes, and interpolation.

Delivers critical guidelines to prevent common bugs, memory leaks, and deprecated patterns in Phoenix LiveView module development, testing, and interactive features.

Essential tool for Elixir/Phoenix/Ash development, enabling live code evaluation, module navigation, SQL execution, and schema introspection.

Guides PostgreSQL data layer configuration for Ash Framework, including migrations, constraints, and multitenancy setup.

Provides integration guidelines for Ash Framework with Phoenix, covering form creation, validation, and error handling for Ash resources and LiveViews.

Facilitates interview-driven project specification creation to clarify requirements, architecture, and scope prior to development work.

Generates robust Prisma seed scripts with built-in error handling and validation for reliable database initialization.

Generates Next.js 16 API route handlers for RESTful CRUD operations with Zod validation.

Automates frontend project scaffolding for multiple frameworks and configurations, including UI library setup and architecture patterns.

Defines TypeScript types, interfaces, and utilities for creating type-safe definitions including DTOs, API responses, and component props.

Creates Prisma schema models with relations and indexes for efficient database schema design and entity relationships.

Reviews code for quality, security, performance, and best practices in software development.

Systematically identifies and resolves code errors, including build, lint, and runtime issues through structured analysis.

Generates Jest-based integration tests for API routes and database operations to validate backend application logic.

Adds support for new AI providers (e.g., Anthropic, Google) or models within recipe generation systems, enabling external LLM integration.

Universal template for implementing category theory structures (functors, monads, etc.) in meta-prompting frameworks to extend with new categorical constructs.

A DSL for building composable prompt templates using categorical structures and functor combinators.

Provides integration patterns for Effect-TS to build AI applications with typed error handling, categorical composition, and structured output generation for LLM calls.

Validates functor, monad, and naturality laws in TypeScript using fp-ts and fast-check for categorical abstractions.

Provides technical details on database schema, authentication flow, environment variables, and service integrations for backend development and API logic.

Provides functional LLM interfaces for Scala applications using ZIO or Cats Effect, enabling type-safe AI pipelines and composable prompt systems.

Automates frontend testing by running browser flows with Playwright, validating user interactions and checking console logs/snapshots.

Enables building MCP servers with categorical composition, typed context management, and composable AI tool ecosystems using category theory patterns.