puzzle: Design & UI for AI Agents | OpenAgentSkill

Overview

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.

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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

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

Design knobs

Knob	Effect	Notes
Grid size / shape	complexity	Square is standard; hex/irregular change feel.
Match/push rule	genre identity	3-in-a-row, shapes, push-into-goal, etc.
Cascade scoring	reward depth	Bigger chains = exponential payoff.
Move / time limit	pressure	Move-limited = puzzly; time = arcade.
Difficulty curve	learning	Introduce one mechanic at a time.
Undo depth	forgiveness	Single-step vs. full history.
Solvability guarantee	fairness	Generated boards must be solvable.
Deadlock handling	no dead ends	Detect 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.

View source ↗

File metadata

name: puzzle
description: >
  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.

View original text

---
name: puzzle
description: >
  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.
---

# 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

| Knob | Effect | Notes |
|------|--------|-------|
| Grid size / shape | complexity | Square is standard; hex/irregular change feel. |
| Match/push rule | genre identity | 3-in-a-row, shapes, push-into-goal, etc. |
| Cascade scoring | reward depth | Bigger chains = exponential payoff. |
| Move / time limit | pressure | Move-limited = puzzly; time = arcade. |
| Difficulty curve | learning | Introduce one mechanic at a time. |
| Undo depth | forgiveness | Single-step vs. full history. |
| Solvability guarantee | fairness | Generated boards must be solvable. |
| Deadlock handling | no dead ends | Detect no-moves; shuffle or end (refs). |

## Patterns

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

```python
# 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)

```python
# 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

```python
# 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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Review before install: Avoid automatic install

License: Apache-2.0

Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: gamedev-skills/awesome-gamedev-agent-skills
License: Apache-2.0
Version: 1.0.0
Last GitHub push: Aug 24, 2026
Registry updated: Sep 2, 2026
Instruction path: skills/genres/puzzle/SKILL.md @ 7110607ab816

Version reported in registry metadata; check source releases before relying on it.

Quality

76/100

Strong

Trust

74/100

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

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More details

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      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "gamedev-skills-puzzle",
      "task": "Use puzzle in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/gamedev-skills-puzzle",
    "api": "https://www.openagentskill.com/api/agent/skills/gamedev-skills-puzzle",
    "audit": "https://www.openagentskill.com/skills/gamedev-skills-puzzle/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=gamedev-skills-puzzle&task=Use%20puzzle%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20puzzle%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20puzzle%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/gamedev-skills-puzzle/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/gamedev-skills-puzzle"
  }
}

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Creator: gamedev-skills
Source: gamedev-skills/awesome-gamedev-agent-skills
Indexed by: OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

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This Registry indexed listing is attributed to gamedev-skills but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/gamedev-skills-puzzle?metric=listed&label=Listed)](https://www.openagentskill.com/skills/gamedev-skills-puzzle?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/gamedev-skills-puzzle?metric=trust&label=Trust)](https://www.openagentskill.com/skills/gamedev-skills-puzzle?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/gamedev-skills-puzzle?metric=audit&label=Audit)](https://www.openagentskill.com/skills/gamedev-skills-puzzle/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/gamedev-skills-puzzle?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/gamedev-skills-puzzle?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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