extracting-keywords
Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.
npx skills add majiayu000/claude-skill-registry --skill extracting-keywords-oaustegard-claude-skills-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Extracting Keywords Extract keywords from text using YAKE (Yet Another Keyword Extractor), an unsupervised statistical keyword extraction algorithm. ## Installation **First time only:** Install YAKE with optimized dependencies to avoid unnecessary downloads. ```bash cd /home/claude uv venv yake-venv --system-site-packages uv pip install yake --python yake-venv/bin/python --no-deps uv pip install jellyfish segtok regex --python yake-venv/bin/python ``` This reuses system packages (numpy, networkx) instead of downloading them (~0.08s vs ~5s). ## Stopwords Configuration **Built-in YAKE stopwords (34 languages):** Use `lan="<code>"` parameter - See Parameters section below for all 34 supported language codes - English (`lan="en"`) is the default **Custom domain stopwords (bundled in `assets/`):** **AI/ML:** `stopwords_ai.txt` - English stopwords + 783 AI/ML domain-specific terms (1357 total) - Filters AI/ML methodology noise (model, training, network, algorithm, parameter) - Filters ML boilerplate (dataset, baseline, benchmark, experiment, evaluation) - Filters technical terms (transformer, embedding, attention, optimization, inference) - Includes full lemmatization (train/trains/tra
- Installation
- Stopwords Configuration
- Basic Usage
- Domain-Specific Extraction
- Using Life Sciences Stopwords
- Using AI/ML Stopwords
- Deeper Extraction (n=2 + n=3 Combined)
- Parameters
- Workflow Patterns
- Single Document Analysis
- Comparing Stopwords Strategies
- Batch Processing
- Multilingual Extraction
- Output Formats
cd /home/claude uv venv yake-venv --system-site-packages uv pip install yake --python yake-venv/bin/python --no-deps uv pip install jellyfish segtok regex --python yake-venv/bin/python Copy life sciences stopwords to YAKE package cp assets/stopwords_ls.txt /home/claude/yake-venv/lib/python3.12/site-packages/yake/core/StopwordsList/stopwords_ls.txt Use with lan="ls" kw_extractor = yake.KeywordExtractor(lan="ls", n=3, top=20)
What does the extracting-keywords skill do?
Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill extracting-keywords-oaustegard-claude-skills-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.