| > **Abandoned path:** We gave up on this as a from-scratch game-generation path. |
| > This knowledge base remains here as reference/source material for the pivot, |
| > especially for remixing known-good templates instead of generating games from |
| > nothing. |
|
|
| # Knowledge base — `game-patterns.txt` |
|
|
| `game-patterns.txt` is the agent's game-development knowledge base: a code-forward |
| reference of single-file HTML game patterns plus compact complete exemplars (game loop, |
| canvas + DPR sizing, input, collision, physics, entities, HUD, tilemaps, audio, a minimal |
| three.js boilerplate, GOTCHAS, and 10 playable genre examples). ~49 KB / ~12.3K tokens. |
|
|
| ## Where it came from |
| It was **distilled** from the vendored `game-engine` skill in |
| [`../skills/game-engine/`](../skills/game-engine/), which is itself vendored from the |
| [`github/awesome-copilot`](https://github.com/github/awesome-copilot) repository (credit and |
| license noted in [`../skills/README.md`](../skills/README.md)). We kept only the ~20% that |
| helps a small model write **single-file, no-build** browser games (raw Canvas 2D + three.js |
| via CDN importmap) and dropped the rest (publishing/marketing, glossary, architecture |
| essays, Phaser/Babylon/A-Frame, Haxe, multi-file project tooling). |
|
|
| ## Complete compact exemplars |
| The `Complete Compact Game Exemplars` section gives the small model concrete games to |
| imitate when patterns alone are not enough. Standalone copies live in `examples/` so they |
| can be opened and tested directly: |
|
|
| - `top-down-racer.html` |
| - `pseudo-3d-racer.html` |
| - `canvas-platformer.html` |
| - `snake.html` |
| - `tetris.html` |
| - `sokoban.html` |
| - `raycaster-fps.html` |
| - `three-space-flight.html` |
| - `canvas-space-shooter.html` |
| - `three-obstacle-runner.html` |
|
|
| Each exemplar is intentionally compact: one HTML file, inline CSS/JS, no build step, |
| visible HUD, working controls, actual mechanics, and retry/win/lose behavior. |
|
|
| ## How the agent uses it (Traditional RAG) |
| `agents.py` embeds this file into a **LanceDB** vector store (`db/lancedb/`) with a local |
| `sentence-transformers` embedder, and the agent runs **Traditional RAG** |
| (`search_knowledge=False`, `add_knowledge_to_context=True`): the patterns relevant to each |
| request are searched and injected straight into the prompt — the model never calls a search |
| tool to fetch them. |
|
|
| ## Editing / regenerating |
| - Edit `game-patterns.txt` directly to change what the model gets. |
| - If an exemplar changes, update both `game-patterns.txt` and the matching file in |
| `examples/`. |
| - After editing, **delete `db/lancedb/`** so the patterns are re-embedded on next startup |
| (insertion uses `skip_if_exists=True`, so unchanged content is not re-embedded). |
| - Keep examples compact — retrieved chunks share the model's context window with chat |
| history and the game it has to generate. |
|
|
