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puzzle

Build grid and board puzzle games such as match-3 and sokoban. Define legal moves, resolve matches and chains, track scores, and restore previous states with undo, while keeping board logic separate from animations.

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Build a puzzle game: grid/board state, move input, rule-based resolution (match-3 cascades, sokoban pushes, tile logic), scoring, and undo. Use for a match-3, sokoban, or grid-logic puzzle.

Run score Not tested

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Good for

  • Implementing match-3 chains
  • Building sokoban pushing rules
  • Adding undo for board, score, and move count
  • Designing solvable puzzle levels

Files included (2)

  • SKILL.md6.8 KB
  • references/board-and-resolution.md5.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Puzzle

A playbook for grid/board puzzle games — the board model, move input, rule resolution (matching, pushing, logic), scoring, undo, and level progression. This is a compositional skill: it models board state and rules and presents them through a tilemap/UI. It does not re-teach tilemaps; it defines the resolution loop and the correctness rules (clean state, deterministic resolution, undo) that keep a puzzle fair and bug-free.

When to use

  • Use when the game is a discrete board the player changes with moves, and the board resolves by rules: match-3/tile-matching, sokoban/block-pusher, sliding puzzle, logic grid.
  • Use when designing match/cascade resolution, undo, level progression, or solvability.

When not to use: real-time grid action with permadeath → roguelike. Card zones/turns → card-game. Physics-based "puzzle platformer" → platformer + physics-tuning. For the tile rendering, use godot-tilemap / unity-tilemap-2d.

Core loop

Read the board → plan a move → make the move → the board resolves by its rules (match, push, fall, fill, cascade) → see progress toward the objective → repeat until solved/failed. The fun is the planning; the engine's job is to resolve each move deterministically and present it clearly.

Must-have systems

  1. Board model — a grid of cells holding pieces; the single source of truth (logic, not visuals).
  2. Move input — swap, push, drag, rotate, or place; validate legality before applying.
  3. Rule resolution — detect and apply the genre's rule (matches, pushes, logic) until stable.
  4. Cascades/chains — when resolution changes the board, re-resolve until no more changes.
  5. Objectives + scoring — win/lose conditions (score, clear all, reach goal); move/time limits.
  6. Undo — revert the last move (and its resolution) exactly; essential for thinky puzzles.
  7. Level progression + (often) generation — hand-authored or generated solvable boards.
  8. Feedback ("juice") — clear, satisfying animation/sound for matches, falls, and chains.

Design knobs

KnobEffectNotes
Grid size / shapecomplexitySquare is standard; hex/irregular change feel.
Match/push rulegenre identity3-in-a-row, shapes, push-into-goal, etc.
Cascade scoringreward depthBigger chains = exponential payoff.
Move / time limitpressureMove-limited = puzzly; time = arcade.
Difficulty curvelearningIntroduce one mechanic at a time.
Undo depthforgivenessSingle-step vs. full history.
Solvability guaranteefairnessGenerated boards must be solvable.
Deadlock handlingno dead endsDetect no-moves; shuffle or end (refs).

Patterns

1. Board model + match detection (logic separate from visuals)

# Pseudocode. The board is the truth; rendering reads from it. (0,0) top-left, y grows down.
board = [[piece_or_empty for _ in range(W)] for _ in range(H)]

def find_matches(board):
    matched = set()
    for y in range(H):                       # horizontal runs of >= 3 equal pieces
        run = 1
        for x in range(1, W):
            if board[y][x] and board[y][x] == board[y][x-1]: run += 1
            else:
                if run >= 3: matched |= {(y, k) for k in range(x-run, x)}
                run = 1
        if run >= 3: matched |= {(y, k) for k in range(W-run, W)}
    # ... repeat the same scan vertically (columns) ...
    return matched

2. Resolve → collapse → refill → cascade (repeat to stability)

# Pseudocode. One player move can trigger a chain; loop until the board stops changing.
def resolve(board):
    chain = 0
    while True:
        matches = find_matches(board)
        if not matches: break                 # stable: resolution complete
        chain += 1
        score += score_for(matches, chain)    # later chain steps score more (see refs)
        clear(board, matches)                  # remove matched pieces
        apply_gravity(board)                   # pieces fall into the gaps
        refill(board, rng)                      # spawn new pieces at the top (seeded RNG)
    return chain

3. Undo via state snapshot or command

# Pseudocode. Snapshot before each move; undo restores it exactly (board + score + counters).
def make_move(move):
    history.append(snapshot(board, score, moves_left))   # push BEFORE applying
    apply(move); resolve(board); moves_left -= 1

def undo():
    if history:
        board, score, moves_left = history.pop()         # exact revert, including resolution

For large boards prefer the command pattern (store the move + enough to invert it) over full snapshots to save memory; snapshots are simplest and fine for small boards.

Pitfalls / failure modes

  • Mixing logic and visuals → animations desync from state and cause bugs. The board model is the single source of truth; the view only renders it.
  • Resolving only once → cascades/chains are missed. Loop resolution until the board is stable (Pattern 2).
  • Undo that doesn't restore everything → score/move-count/random-state drift. Snapshot all state, or make the move fully invertible.
  • Unseeded refill RNG → can't reproduce a level / no deterministic undo or daily puzzle. Seed it.
  • Generated boards that aren't solvable → unfair dead ends. Generate-and-verify, or generate from a known solution backward (refs).
  • No deadlock detection (match-3) → board with no valid moves softlocks. Detect "no moves" and shuffle or end the level (refs).
  • Difficulty spikes → too many mechanics at once. Teach one mechanic per level before combining.
  • Resolution mid-animation accepts input → double-moves/corruption. Lock input until the board is stable.

Composition (build it from these skills)

  • Board rendering: godot-tilemap / unity-tilemap-2d for the grid; godot-ui-control for HUD, score, and menus.
  • Levels: level-design for hand-authored puzzles and difficulty pacing; procedural-gen for solvable generated boards.
  • Persistence: save-systems for level progress, high scores, and seeded daily puzzles.
  • Juice: game-feel for match/cascade pop, screen shake, and chain feedback; the engine animation/Tween skill for swaps/falls/clears; audio-design for match and chain cues.
  • Scripting: godot-gdscript / unity-csharp-scripting for the resolution loop and rules.

References

  • For match-3 detection/gravity/refill/cascade detail, deadlock detection and reshuffles, sokoban/rule-based puzzles, undo strategies, solvable generation, and scoring, read references/board-and-resolution.md.

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