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Puzzle

OrganizationPopular
gamedev-skills
puzzle

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.

Overview

Publishergamedev-skills
Repositoryawesome-gamedev-agent-skills
Skill namepuzzle
Stars
1.3K
Forks
107
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gamedev-skills on GitHub. Read the source before you install it.

Installation

Install the Puzzle AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gamedev-skills/awesome-gamedev-agent-skills/tree/main/skills/genres/puzzle
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gamedev-skills/awesome-gamedev-agent-skills.git /tmp/awesome-gamedev-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-gamedev-agent-skills/skills/genres/puzzle .claude/skills/puzzle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Puzzle in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Puzzle on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Puzzle is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Puzzle AI skill do?

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.

Why use Puzzle on TypingMind?

Because you install it once and use it with any model. Puzzle is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Puzzle in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gamedev-skills/awesome-gamedev-agent-skills/tree/main/skills/genres/puzzle. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Puzzle?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Puzzle?

As many as you like. As long as a model supports skills, you can use Puzzle with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Puzzle AI skill free?

Yes. It is published on GitHub by gamedev-skills under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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