Procedural Gen

Procedural generation

Generate levels, terrain, and loot from compact rules and a seed. The throughline of good procgen is determinism: a single seed reproduces the same world, so bugs are repeatable and players can share seeds. This skill owns the core algorithms — noise, seeded RNG, dungeon layout, weighted tables; genres like roguelike and survival-crafting consume it.

When to use

• Use to generate maps, dungeons, terrain heightmaps, item drops, or any content

you do not want to author by hand.

• Use when results must be reproducible from a seed (debugging, daily

challenges, shareable worlds).

• Use to pick weighted random outcomes (loot rarity, spawn tables).

**When not to use:** for the engine's tile API to paint the result, use godot-tilemap or unity-tilemap-2d. For routing AI through the generated map, use game-ai. For carefully hand-paced levels, use level-design — procgen and authored design are complementary, not interchangeable.

Core workflow

1. Own your randomness. Create one seeded RNG instance and pass it

everywhere. Never call the global/static random in generation code — it makes results irreproducible and order-dependent.

1. Pick the technique for the content. Continuous terrain/heightmaps → noise.

Discrete rooms/corridors → space partitioning or agent-based carving. Outcomes with rarities → weighted tables.

1. Generate into a plain data grid/array first, decoupled from rendering.

Generation fills int[][] or a dict; a separate pass draws it.

1. Validate before shipping the result to the player. Is every room

reachable? Is the spawn safe? Is there a path to the exit? Reject or repair layouts that fail; do not hand the player a broken map.

1. Tune with the seed fixed so each parameter change is visible in isolation,

then sweep seeds to check the distribution, not just one lucky map.

Patterns

1. Seeded, deterministic RNG (the foundation)

import random
rng = random.Random(seed)        # a dedicated instance — NOT the global random.*
room_count = rng.randint(5, 12)  # same seed -> same sequence, every run
# RIGHT: thread `rng` through every function that makes a choice.
# WRONG: calling random.randint(...) (global state) — order-dependent, unseedable.

Engine equivalents: Godot var rng = RandomNumberGenerator.new(); rng.seed = s; Unity var rng = new System.Random(seed) (or UnityEngine.Random.InitState). Store the seed in the save file so a world can be regenerated.

2. Fractal (fBm) noise for heightmaps

# Sum several octaves: each higher octave has higher frequency, lower amplitude.
def fbm(noise, x, y, octaves=5, lacunarity=2.0, gain=0.5):
    total, amp, freq, norm = 0.0, 1.0, 1.0, 0.0
    for _ in range(octaves):
        total += amp * noise(x * freq, y * freq)   # noise() returns ~0..1
        norm  += amp                                # track total amplitude
        amp   *= gain                               # each octave contributes less
        freq  *= lacunarity                         # ...at a higher frequency
    return total / norm                             # normalize back into 0..1

# Redistribute to carve flat valleys / sharpen peaks: higher exp -> more lowland.
elevation = pow(fbm(noise, nx, ny), 2.2)

Use a real noise library (FastNoiseLite, opensimplex, Unity.Mathematics.noise, or Mathf.PerlinNoise) — do not implement gradient noise yourself. Seed elevation and moisture with different seeds so a biome lookup over both fields isn't perfectly correlated. Full biome lookup and island shaping are in references/noise.md.

3. Weighted loot table (rarity-correct selection)

# Roll proportional to weight: common drops far more often than legendary.
def weighted_pick(rng, table):           # table: list of (item, weight)
    total = sum(w for _, w in table)
    roll = rng.uniform(0, total)          # a point on the cumulative line
    upto = 0.0
    for item, w in table:
        upto += w
        if roll < upto:                   # first bucket the roll falls into
            return item
    return table[-1][0]                   # float-safety fallback

loot = weighted_pick(rng, [("common", 70), ("rare", 25), ("legendary", 5)])

Weights need not sum to 100 — they are relative. To prevent bad streaks, use a "pity"/bag system (see references/dungeon-generation.md notes on distributions).

4. Rooms-and-corridors dungeon (sketch)

# 1. Place non-overlapping rooms; 2. connect them; 3. carve into the grid.
rooms = []
for _ in range(attempts):
    r = Rect(rng.randint(1, W-w-1), rng.randint(1, H-h-1), w, h)
    if not any(r.intersects(o.expand(1)) for o in rooms):  # keep a 1-tile gap
        rooms.append(r)
for a, b in zip(rooms, rooms[1:]):       # connect each room to the next
    carve_l_corridor(grid, a.center, b.center, rng)   # horizontal then vertical

The complete generator (BSP partitioning, L-corridors, reachability check, and random-walk caves) is in references/dungeon-generation.md.

Pitfalls

• Using the global RNG inside generation makes worlds unreproducible and

breaks the moment call order changes. Always pass a seeded instance.

• Correlated noise fields: sampling elevation and moisture from the same

seed/offset produces biomes that line up in bands. Offset or reseed each field.

• Octave artifacts: adding octaves without renormalizing pushes values out of

0..1; divide by the summed amplitude (and beware library output ranges — some return -1..1, some 0..1).

• No connectivity check: rooms or caves can end up isolated. Flood-fill from

the spawn and discard/reconnect unreachable regions before play.

• Unbounded placement loops: "keep trying until N rooms fit" can spin forever

on a small grid. Cap attempts and accept fewer rooms.

• Seeding once globally, then relying on frame timing: any non-deterministic

input (time, physics, hash randomization) leaking into generation destroys reproducibility.

References

• references/noise.md — octaves/lacunarity/gain, redistribution, island

shaping, two-axis biome lookup, blue-noise object scatter.

• references/dungeon-generation.md — BSP, rooms+corridors, random-walk caves,

cellular-automata smoothing, connectivity validation, distribution/pity tables.

Related skills

• godot-tilemap, unity-tilemap-2d — paint the generated grid into the engine.
• game-ai — pathfinding over the generated graph.
• level-design — pacing and hand-authored structure that procgen complements.
• roguelike, survival-crafting — genres that compose this skill.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: gamedev-skills
• Source: gamedev-skills/awesome-gamedev-agent-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.