Puzzle

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

Puzzle专用技能,帮助AI Agent高效完成相关任务。

一句话选型结论

按资料字段整理

Puzzle是AI 工具类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类AI 工具 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_3c6cb52e/puzzle-p2s0
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

AI 工具
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_3c6cb52e仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791682766
正文体量7.1 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
组合多个 AI 能力

把写作、检索、总结串成一条流程。

搭工作流

把固定流程做成可重复调用的步骤。

做提示词与评测

整理提示词并对比输出效果。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在AI 工具场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
Puzzle当前 未标注权限 未标注 未标注 未标注 有
执行计划 只读 GitHub 仓库 151.5 万 Codex、Claude、Cursor 有
Playwright MCP 测试 浏览器控制 ClawHub 市场 136.5 万 OpenClaw 有
完成前的验证 未标注权限 GitHub 仓库 131.9 万 Codex、Claude、Cursor 有
子 Agent 驱动开发 只读 GitHub 仓库 131.2 万 Codex、Claude、Cursor 有
前端设计 执行命令 GitHub 仓库 125.6 万 Codex、Claude、Cursor 有

SKILL.md 全文

7.1 KB
模型实际读到的说明书原文,来自技能仓库

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)

# 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.

常见问题

5 问
答案由本站依据条目字段整理
Puzzle怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_3c6cb52e/puzzle-p2s0。安装方式为未标注。装完刷新技能列表或重开客户端。

Puzzle支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

Puzzle权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

Puzzle收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

Puzzle用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。