README.md

metadata

license: other
task_categories:
  - text-generation
tags:
  - agentic-sft
  - planning
  - godot
  - phaser

EvoGame plan corpus, distilled from finished games

908 plan-stage SFT rows distilled from finished games — 832 Godot, 76 web — across 17 archetype families. Each row is brief -> file-level implementation plan.

This exists because plan data does not need a recorded agent session. The corpus these came from holds ~900 finished games with no trajectory files, which made them useless for generation training but perfectly usable here: the brief comes from the task definition, and the plan is read off the finished file tree.

Row format

Byte-identical in shape to the original 454-row plan.jsonl, so the two pool cleanly:

{
  "task": "cardgame-arcane-rail",
  "archetype": "cardgame",
  "model": "gpt-5.5",
  "n_chars": 3067,
  "engine": "godot",
  "source": "basegen_unpublished",
  "messages": [
    {"role": "system",    "content": "You are a senior game engineer. Given a game brief, produce a file-level implementation plan: which files to MODIFY and which to CREATE, with a one-line reason for each."},
    {"role": "user",      "content": "<the game brief>"},
    {"role": "assistant", "content": "Implementation plan before coding:\n\n- **Files to MODIFY**\n  - `path`: reason\n- **Files to CREATE**\n  - ..."}
  ]
}

engine and source are added for provenance; only messages is trained on.

How it was distilled, and what makes it honest

Two rules, both about not writing fiction:

• The plan is read off the ACTUAL file tree, never off a narration. The distiller receives the brief plus the real file listing and excerpts of the main source files. A game whose README claimed a physics system it does not contain would otherwise have that claim copied into the training target.
• Only COMPLETE games are distilled. Godot needs project.godot plus at least one script or scene; web needs package.json plus a src/ tree. A plan distilled from a half-built game is a plan for half-building.

The distiller prompt is engine-specific. A Phaser prompt pointed at a Godot game produces plans full of TypeScript files that do not exist, so Godot runs are told the vocabulary is .tscn / .gd / project.godot and web runs are told it is src/*.ts / package.json / vite.

Verification

Every backticked path in every plan was checked against the real game's file tree:

923 games attempted
922 distilled   (1 skipped: incomplete game; 0 API failures; 0 malformed)
 14 dropped     (invented paths, or wrong engine vocabulary)
908 kept

23,785 file paths referenced
     0 not found in the game            (0.0%)
     0 rows with wrong engine vocabulary

The 14 dropped rows include a few false positives from a deliberately strict checker — res://Main.tscn is valid Godot syntax and src/ is a directory, not a file — but at 1.5% of the corpus, dropping them is cheaper than reasoning about which are safe.

Composition

	this corpus	original plan.jsonl
rows	908	454
engines	Godot 832, web 76	web only
archetype families	17	15
plan length	1,310 – 7,228 chars (p50 2,791)	908 – 3,106 (p50 2,041)

Top families: simulation 127, platformer 90, cardgame 89, horror 81, roguelike 55, strategy 54, towerdefense 47, sports 43.

Note the length difference. Web games carry ~64 files against Godot's ~20–32, so their plans run longer than anything in the original corpus. The content is verified accurate, so files were not trimmed out to hit a length target — but pooling the two shifts the length distribution upward, and that is a property to know rather than a defect to hide.

Caveat if you pool this with generation data

The original stage-2 mix was 409 gen + 454 plan, and the concern that motivated a reweighting experiment was that generation was being outvoted at 47%. Adding these 908 rows takes plan to 1,362 against 409 gen — generation falls to 23%. If that concern was ever real, this makes it substantially worse, not better. Consider the gen:plan ratio deliberately rather than pooling everything by default.

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Pipeline code: WenyiWU0111/OpenGame-reproduce · evogame-sft-pipeline/ · evogame-data-pipeline/

Companion repos: adapters · training corpora · eval results · raw trajectories