This page is a living technical record of skills, tools, and engineering practices demonstrated in this project.

Project Overview

Sketches is a collection of generative art sketches implemented in p5.js, showcasing a variety of visual styles and techniques. The project is structured to support iterative development and experimentation with creative coding concepts, while also demonstrating a range of software engineering skills and best practices.

At a Glance

  • Project Type: Creative Coding / Generative Art
  • Primary Runtime: Node.js
  • Rendering Library: p5.js
  • Primary Implementation Language: TypeScript
  • Automation: GitHub Actions workflow for build and test validation
  • Dependency Automation: Automated dependency update management with Dependabot
  • Documentation Pattern: Source-linked evidence with generated API docs via TypeDoc

Skills and Tooling Inventory

Capability Record

  • Interface-driven architecture for sketch implementations
  • Class-based visual logic organized around p5 lifecycle hooks
  • Typed entrypoint composition and runtime binding
  • Schema-driven type definitions using TypeBox and Zod
  • Runtime type validation via discriminator pattern across multiple schema libraries
  • Static validator utilities for number and string types
  • Parameterized unit testing with Vitest
  • CI-based lint, build, and test verification
  • Automated static site deployment and project documentation generation
  • AI-assisted development and pair programming using GitHub Copilot workflows

Detailed Technical Notes

Interface-driven sketch model

  • A shared Sketch interface defines the implementation contract for sketch modules.
  • This keeps sketch integration consistent and reduces coupling between entrypoint and sketch logic.
  • Evidence:

Entrypoint composition

  • Application startup instantiates a sketch implementation and binds its main function to p5.
  • This keeps orchestration logic centralized and minimal.
  • Evidence:

Sketch implementation pattern

Quality validation workflows

  • Build and test validation is configured through a GitHub Actions workflow file.
  • This workflow runs across multiple Node.js versions to improve compatibility confidence.
  • Evidence:

Documentation and delivery workflows

AI-assisted development with GitHub Copilot

  • GitHub Copilot is used for AI-assisted coding workflows, including code generation, refactoring, and implementation of new features via natural language prompts.
  • Copilot pair programming is used interactively during development to accelerate iteration and maintain code quality.
  • Evidence:

Schema-driven type definitions

Runtime type validation via discriminator pattern

Static validator utilities

  • NumberValidator and StringValidator are static utility classes that expose reusable validation predicates and regular expression patterns (e.g., hex color format matching, single-line lowercase trimmed string validation).
  • All static utility classes (including Discriminator) use a private constructor with a runtime throw to prevent instantiation, ensuring they are always used as namespaces rather than objects. Runtime constructor guards are covered by unit tests.
  • Evidence:

Parameterized unit testing with Vitest

Current Gaps / Future Improvements

  • Sketch coverage is intentionally narrow at this stage (single runtime-wired sketch); expand to additional sketches to demonstrate broader rendering patterns.
  • End-to-end/browser-level tests are not yet implemented; current validation is focused on unit tests for shared library modules.
  • Portfolio evidence coverage can be strengthened by adding direct links to workflow trigger blocks and bundling/output configuration where claims reference CI triggers or build output behavior.
  • Documentation publishing remains partly manual (TypeDoc output copied into the docs site); automate this handoff to reduce maintenance overhead.