Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Boggle Solver
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).
skill openclawclawhub Free
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).
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, data/README.md, data/download.py, scripts/solve.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- christianhaberl
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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)
Read the 4x4 grid from the photo (left-to-right, top-to-bottom) Show the grid to the user and ask for confirmation before solving Only after user confirms → run the solver Always run English and German SEPARATELY — present as two labeled sections (🇬🇧 / 🇩🇪)
Solve a board
# 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: python3 skills/boggle/scripts/solve.py --letters ELMUZBTSETVOCKNA --lang en
Options
FlagDescription--lang en/deLanguage (default: en; always run EN and DE separately)--min NMinimum word length (default: 3)--jsonJSON output with scores--dict FILECustom 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Scripts
- SKILL.md
- data/README.md
- README.md
- data/download.py
- scripts/solve.py