Portfolio Skills
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
- Languages: TypeScript, JavaScript, HTML, CSS, Markdown, YAML
- Runtime & Libraries: Node.js, p5.js
- TypeSafety & Validation: TypeBox, Zod
- Testing: Vitest
- Bundling: webpack
- Code Quality: ESLint
- Documentation: TypeDoc
- Site Generation: Bundler, Jekyll, Liquid
- Dependency Management: npm
- Versioning & Platform: Git, GitHub
- Automation: GitHub Actions
- Hosting & Deployment: GitHub Pages
- Code Analysis / Security: CodeQL
- Dependency Automation: Dependabot
- Environment Management: n, rbenv
- Development Environments: WebStorm, Visual Studio Code
- AI-Assisted Development: GitHub Copilot
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
Sketchinterface 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
mainfunction to p5. - This keeps orchestration logic centralized and minimal.
- Evidence:
Sketch implementation pattern
- Sketch behavior is encapsulated in class methods mapped to p5 lifecycle hooks (
setup,draw). - This supports focused iteration on visual behavior while preserving shared conventions across sketch implementations.
- Evidence:
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
- TypeDoc is configured for API documentation generation.
- GitHub Pages site delivery is configured through the Jekyll workflow and the
docs/site structure. - Evidence:
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 types for domain objects (
Discriminable,Palette,PaletteColor,AspectRatioConfig) are derived directly from TypeBox or Zod schemas usingStatic<typeof Schema>orz.infer<typeof SCHEMA>. - This eliminates duplication between the type definition and its validation logic and ensures that compile-time types and runtime validation stay in sync.
- Evidence:
Runtime type validation via discriminator pattern
- The
Discriminatorstatic class validates objects at runtime using a two-step approach: first checking adiscriminatorfield for a type tag match, then applying the full TypeBox or Zod schema parse. - This combines lightweight tag-based narrowing with exhaustive schema validation, and works uniformly across both schema libraries.
- Evidence:
Static validator utilities
NumberValidatorandStringValidatorare 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 runtimethrowto 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
- Unit tests use
test.eachfor parameterized cases, covering valid and invalid inputs including boundary values (zero, negative numbers, non-finite values, empty strings, non-string types, untrimmed strings, multiline strings, and single-line lowercase/uppercase/mixed-case strings). - Shared test input constants and test-case builder utilities are centralized under
test/utils/and reused across test suites to keep coverage consistent and reduce duplication. - Evidence:
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.