topic-modeler
Extract topics from text collections using LDA (Latent Dirichlet Allocation) with keyword extraction and topic visualization.
Profile →npx skills add majiayu000/claude-skill-registry --skill topic-modeler-dkyazzentwatwa-chatgpt-skills-2 --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.
# Topic Modeler Extract topics from text collections using LDA. ## Features - **LDA Topic Modeling**: Latent Dirichlet Allocation - **Topic Keywords**: Extract representative keywords per topic - **Document Classification**: Assign documents to topics - **Visualization**: Topic word clouds and distributions - **Coherence Scores**: Evaluate topic quality ## CLI Usage ```bash python topic_modeler.py --input documents.csv --column text --topics 5 --output topics.json ``` ## Dependencies - gensim>=4.3.0 - nltk>=3.8.0 - pandas>=2.0.0 - matplotlib>=3.7.0 - wordcloud>=1.9.0
- Features
- CLI Usage
- Dependencies
python topic_modeler.py --input documents.csv --column text --topics 5 --output topics.json
What does the topic-modeler skill do?
Extract topics from text collections using LDA (Latent Dirichlet Allocation) with keyword extraction and topic visualization.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill topic-modeler-dkyazzentwatwa-chatgpt-skills-2 --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.