Playtest Audit

playtest-audit

Prove the game can actually be played to a good outcome, not just that its rules are correct. Emit games/<id>/playtest.gd: a headless bot that drives the real game loop with a competent policy and asserts the title is winnable, fair, and lets a player make progress. Prints per-step metrics then exactly PLAYTEST OK (exit 0) or PLAYTEST FAIL: <reason> (exit 1).

Why this exists (the gap it closes)

Every other gate is blind to balance:

• selftest proves rules by driving the engine directly — and that is exactly its blind spot. diver-0001's selftest "proved" a cautious dive banks by calling collect() at depth 0, asserting the economy in the abstract. It never simulated the spatial reality: treasure spawned 270m below the diver, past a crush line at 200m, so a real player physically could not reach a treasure and return alive — the game was 100% unwinnable and every logic/UI gate was green. The owner found it in ten seconds of play.
• uitest proves a tap reaches a handler. visual-audit proves the screen reads.
• the deepen design-depth audit reads the code and reasons on paper — it judged diver "deeper, not bigger" while the same build was unwinnable, because it never played it.

Winnability/fairness/economy-solvency are emergent properties of the assembled loop (spawn geometry × collision × resource drain × difficulty ramp). The only way to see them is to play the real loop. That is this skill.

Loop position

A validation gate, like selftest/uitest — run it after builder and after every deepen pass (tuning is exactly what deepen changes). builder/deepen emit playtest.gd; the validator runs it and fails the build on PLAYTEST FAIL. It records nothing to the manifest — its outputs are the pass/fail gate + a balance-metrics report (and any code/tuning fixes that follow).

Discovery vs. confirmation — where this gate sits relative to generate-and-verify. When a game generates discrete content instances (puzzle boards, procedural maps, wave compositions, shop economies), builder's generate-and-verify gate already guarantees each instance is solvable at creation via an in-game solver (make_verified + Solver). For those games this bot is confirmation, not discovery: it verifies the per-instance guarantee survived assembly — that collision × resource-drain × the difficulty ramp didn't make a provably-solvable instance unwinnable in practice. It stays a hard gate regardless. Where no such upstream gate exists (winnability is a pure tuning/spatial property of the assembled loop — diver-0001's crush line vs. treasure depth), this bot remains the only place winnability is checked, and is in full discovery mode. Either way: a PLAYTEST FAIL is a tuning/generator bug, never license to weaken selftest or Solver.

The harness — drive the REAL loop, deterministically

A SceneTree script that instantiates the actual scene and steps it by hand so the bot sees real spawns, real collision, real resource math:

extends SceneTree
var main
func _initialize() -> void: _run()
func _run() -> void:
    var scene: PackedScene = load("res://Main.tscn")
    main = scene.instantiate()
    root.add_child(main)
    await process_frame          # let _ready() run
    main.set_process(false)      # WE drive _process at a fixed dt — deterministic, no engine variance
    var DT := 1.0 / 60.0
    while main.<active> and guard < CAP:
        <set the bot's intent on the real input surface, e.g. main.target_x = ...>
        main._process(DT)        # the real spawn/move/collide/tick, stepped by us
    # read real state off main.<state> and assert invariants

• Step the real _process yourself with a fixed dt after set_process(false) — never rely on engine frame timing (variable dt, non-determinism). Seed every RNG; with a fixed seed + fixed dt the whole playthrough is reproducible.
• The bot acts through the real input surface the game already exposes (the steer target, the action methods) — not by reaching past the rules. If you must poke private state to set up a scenario, do it sparingly and say so.
• Reuse the genre's existing headless boot assumptions (no autoloads under --script, preload data layers — same gotchas as selftest/uitest).

The bot — COMPETENT, not optimal

Model a reasonable player, not a theoretical-best solver and not a random flailer:

• A simple greedy-but-cautious policy (head for the nearest reward; retreat when the resource budget only just covers a safe exit, with a margin).
• If a competent, cautious player cannot earn / progress / survive, the tuning is broken — that is the finding. (diver's bot turned back at a safe margin and still banked 0, because nothing was reachable.)
• Keep the bot's competence honest: a deliberately weak bot false-fails good games; an optimal one hides difficulty. When the bot is weaker than a human at a sub-skill (precise dodging/aiming), REPORT what it couldn't do rather than FAIL on it — flag it for the human playtest instead of gating on it (diver: the crude lerp-steering bot couldn't grab the sparse Trench treasures, so deep-commission fill is reported, not failed).

Invariants to assert (instantiate per genre)

Gate on the ones that mean "a player can succeed":

• Solvency / winnable. A competent run reaches a good outcome at least sometimes: earns positive currency, clears the encounter, survives a wave, scores above zero. The anti-unwinnable gate. (diver: a careful rig-0 dive banks > 0.)
• First goal reachable. The first objective/commission/boss/quota is achievable from the starting loadout — not gated behind a resource you can only get by doing it (a deadlock).
• Fairness / no death-spiral. The player never dies on a turn where they played safely (no unavoidable hit, no spawn you cannot react to); a setback is recoverable, not terminal.
• Progression solvency. Across several sessions (buying upgrades / advancing), resources accumulate and the next tier is affordable — measured as gross earnings, not net balance (a bot that spends on upgrades has a low balance while clearly progressing).
• No trivial dominant win. A single repeated cheap action does not run the score to infinity (the inverse failure — the loop is solved). Pairs with deepen's dominant-strategy test.

Metrics to report (every run, pass or fail)

Print enough to tune from, not just a verdict: earnings/clear per session, the minimum resource margin seen (how close to death a careful player runs), depth/wave/turn reached, time-to-first-upgrade, commission/objective fill rate. These numbers are how the owner sets difficulty — surface them even when the run passes.

Machine-readable contract (the tuning signal). In addition to the human lines and the PLAYTEST OK/FAIL verdict, print exactly one final line PLAYTEST METRICS {json} carrying the numbers above plus the invariant booleans (solvent, first_goal_reachable, no_death_spiral, no_trivial_dominant) so they coincide with the gate verdict. This line is the contract deepen's balance-search (tools/balance.mjs) consumes: it aggregates the metrics across K seeds, hard-rejects configs whose invariants are false, and scores the rest against target bands. The metrics are not just a human dump — they are the tuning oracle. Nothing new is simulated to produce the line; it serialises what the bot already computed.

Honest reporting

Gate on the winnability invariants; report (don't FAIL) anything that's a bot-skill limitation rather than a game flaw, and say which is which. A PLAYTEST OK must mean "a competent player can succeed," and the metrics must name any difficulty the bot only barely cleared — never let a silent cap read as "fully balanced."

Gotchas (carry these)

• A logic self-test that drives the engine directly cannot see spatial/economic winnability — that is precisely why this gate exists. Do not "fix" a PLAYTEST FAIL by weakening selftest; fix the tuning (spawn geometry, gate depths, costs, the ramp).
• Stepping _process manually while the engine also auto-runs it double-ticks the world — set_process(false) first.
• Manual stepping means _draw never runs, so RNG used only in _draw (shake/parallax) won't desync the spawn RNG — keep spawn RNG in _process for a reproducible run.
• Clear any user:// save at start AND end (a persisted economy leaks across runs and makes "can a fresh player win?" non-deterministic).

Dogfood lesson (diver-0001)

The bug that motivated this skill: crush line (200m) shallower than both the commission zone (Reef@240m) and the nearest treasure spawn (332m), so banking anything was impossible while every logic/UI/design gate stayed green. The bot caught it on the first run (earned 0 across 8 dives). The fix was pure tuning — collectible treasure pre-seeded into safe water, the rig-0 safe depth raised to cover the Shallows + first commission, deeper zones pulled up — verified by re-running until PLAYTEST OK (dive 1 banks 168 incl. a filled commission).