The Problem
Creating custom content at scale was easy to get wrong in subtle ways. A script could parse successfully and still behave incorrectly in-game. Tokens could be missing or mismatched. Naming collisions, packaging mistakes, stale compatibility assumptions, and upstream engine changes could turn a large import into a long manual debugging session.
I built Forge-Navi to make the scripting side repeatable and testable, then split visual asset production into Token-Navi so each system could have a clear responsibility.
System Design
Forge-Navi
Owns card and token semantics, Forge scripting, validation, repair workflows, compatibility knowledge, dependency checks, project state, and final release gates.
Token-Navi
Owns the visual-production pipeline for required token and card faces, including rendering, structural QA, approval tracking, and verified delivery back into the release pipeline.
What I Built
Semantic Validation
Validation goes beyond syntax. The core rule is to prove that a card behaves correctly, not merely that its script parses.
Reusable Knowledge
Verified patterns, known engine traps, prior fixes, and failure classes are retained so later sets can reuse proven solutions instead of rediscovering the same bugs.
Release Gates
Work moves through explicit audit states rather than being called finished early. Final assembly is treated as a candidate until an independent full-set audit passes.
Token Handoff Contract
Forge-Navi describes exactly what a custom token needs. Token-Navi produces and approves the visual, then returns immutable assets for verified packaging.
Upstream Compatibility
Current Forge behavior is tracked separately from stable project policy so engine changes can be absorbed without rewriting the whole system.
Automation & Tooling
The workflow uses scripted installers, validation tools, project-state tracking, repeatable repair passes, packaging checks, and regression-oriented QA.
Scale
The system has been used across a working library of more than 5,000 custom card scripts, hundreds of token definitions/assets, and dozens of custom sets. That scale is what pushed the project from ad-hoc scripting into a real production pipeline with checkpoints, repair queues, dependency contracts, and release criteria.
What This Project Demonstrates
- PowerShell and scripting automation
- Debugging and root-cause analysis
- Static, semantic, and regression-oriented QA
- Stateful production workflows and release gates
- Dependency and asset-pipeline design
- Compatibility work against an actively changing upstream project
- Separating responsibilities between cooperating tools
- Turning repeated manual failures into reusable automation
Why the Repositories Stay Private
The portfolio is intentionally the public-facing layer. The production repositories include internal knowledge bases, active project state, generated content, runtime assets, and work-in-progress tooling. Keeping those private lets me show the engineering decisions and results without publishing material that does not belong in a public showcase.