Overview
- Skill Key
- christianhaberl/boggle
- Author
- christianhaberl
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/christianhaberl/boggle
- Latest Commit SHA
- baf44279fb4f51f655c643bc7c72599e34ddc8c7
Solve Boggle boards — find all valid words (German + English) on a 4x4 letter grid. Use when the user shares a Boggle photo, asks for words on a grid, or plays word games. Includes 1.7M word dictionaries (DE+EN).
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PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 boggle 技能。 若已安装,则直接安装 boggle 技能。
# Boggle Solver Fast trie-based DFS solver with dictionary-only matching. No AI/LLM guessing — words are validated exclusively against bundled dictionaries (359K English + 1.35M German). ## Workflow (from photo) 1. **Read the 4x4 grid** from the photo (left-to-right, top-to-bottom) 2. **Show the grid to the user and ask for confirmation** before solving 3. Only after user confirms → run the solver 4. **Always run English and German SEPARATELY** — present as two labeled sections (🇬🇧 / 🇩🇪) ## Solve a board ```bash # English python3 skills/boggle/scripts/solve.py ELMU ZBTS ETVO CKNA --lang en # German python3 skills/boggle/scripts/solve.py ELMU ZBTS ETVO CKNA --lang de ``` Each row is one argument (4 letters). Or use `--letters`: ```bash python3 skills/boggle/scripts/solve.py --letters ELMUZBTSETVOCKNA --lang en ``` ## Options | Flag | Description | |---|---| | `--lang en/de` | Language (default: en; **always run EN and DE separately**) | | `--min N` | Minimum word length (default: 3) | | `--json` | JSON output with scores | | `--dict FILE` | Custom dictionary (repeatable) | ## Scoring (standard Boggle) - 3-4 letters: 1 pt - 5 letters: 2 pts - 6 letters: 3 pts - 7 letters: 5 pts - 8+ letters: 11 pts ## How it works - Builds a trie from dictionary files (one-time, ~11s) - DFS traversal from every cell, pruned by trie prefixes - Adjacency: 8 neighbors (horizontal, vertical, diagonal) - Each cell used at most once per word - **Qu tile support:** Standard Boggle "Qu" tiles are handled as a single cell (e.g., `QUENHARI...` → "QU" occupies one position) - **All matching is dictionary-only** — no generative/guessed words ## Data Dictionaries are auto-downloaded from GitHub on first run if missing. - `data/words_english_boggle.txt` — 359K English words - `data/words_german_boggle.txt` — 1.35M German words ## Performance - Trie build: ~11s (first run, 1.7M words) - Solve: <5ms per board
# 🎲 Boggle Solver — OpenClaw Skill Fast trie-based DFS Boggle solver for [OpenClaw](https://github.com/openclaw/openclaw). ## Features - **1.7M dictionary words** — 359K English + 1.35M German - **Qu-tile support** — standard Boggle rules - **< 5ms solve time** per board - **JSON output** with Boggle scoring - **Bilingual** — run English and German separately ## Install ```bash clawdhub install boggle ``` Or manually copy the `skills/boggle/` folder into your OpenClaw workspace. ## Usage ```bash # English python3 scripts/solve.py ELMU ZBTS ETVO CKNA --lang en # German python3 scripts/solve.py ELMU ZBTS ETVO CKNA --lang de # With --letters flag python3 scripts/solve.py --letters ELMUZBTSETVOCKNA --lang en # JSON output python3 scripts/solve.py ELMU ZBTS ETVO CKNA --lang en --json ``` ## Options | Flag | Description | |---|---| | `--lang en/de` | Dictionary language (default: en) | | `--min N` | Minimum word length (default: 3) | | `--json` | JSON output with scores | | `--dict FILE` | Custom dictionary (repeatable) | ## Scoring (standard Boggle) - 3-4 letters: 1 pt - 5 letters: 2 pts - 6 letters: 3 pts - 7 letters: 5 pts - 8+ letters: 11 pts ## How it works - Builds a trie from dictionary files - DFS traversal from every cell, pruned by trie prefixes - Adjacency: 8 neighbors (horizontal, vertical, diagonal) - Each cell used at most once per word - Qu tiles handled as single cell - **All matching is dictionary-only** — no AI guessing ## AI-Reviewed This skill was reviewed by **Codex** and **Gemini Code Assist** across 5 review rounds. All findings addressed. ## License MIT
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