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bertopic

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

Skill by VectorSpaceLab

IndexedMIT★ 341
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/vectorspacelab-bertopic

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About

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

Use cases: data, ml

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/bertopic

What's inside

Skill name
bertopic
Description
Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.
License
MIT
Metadata
disco-role: operating
Instructions
46 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (36)

  • SKILL.md
  • repo-provenance.md · references/
  • repo-routing-metadata.json · references/
  • troubleshooting.md · references/
  • workflows.md · references/
  • check_env.py · scripts/
  • SKILL.md · sub-skills/analysis-visualization/
  • api-reference.md · sub-skills/analysis-visualization/references/
  • troubleshooting.md · sub-skills/analysis-visualization/references/
  • workflows.md · sub-skills/analysis-visualization/references/
  • smoke_visualization.py · sub-skills/analysis-visualization/scripts/
  • SKILL.md · sub-skills/embeddings-backends/
  • api-reference.md · sub-skills/embeddings-backends/references/
  • troubleshooting.md · sub-skills/embeddings-backends/references/
  • workflows.md · sub-skills/embeddings-backends/references/
  • inventory_backends.py · sub-skills/embeddings-backends/scripts/
  • SKILL.md · sub-skills/representations-labeling/
  • api-reference.md · sub-skills/representations-labeling/references/
  • troubleshooting.md · sub-skills/representations-labeling/references/
  • workflows.md · sub-skills/representations-labeling/references/
  • smoke_representations.py · sub-skills/representations-labeling/scripts/
  • SKILL.md · sub-skills/serialization/
  • api-reference.md · sub-skills/serialization/references/
  • troubleshooting.md · sub-skills/serialization/references/
  • workflows.md · sub-skills/serialization/references/
  • smoke_serialization.py · sub-skills/serialization/scripts/
  • SKILL.md · sub-skills/topic-modeling/
  • api-reference.md · sub-skills/topic-modeling/references/
  • troubleshooting.md · sub-skills/topic-modeling/references/
  • workflows.md · sub-skills/topic-modeling/references/
  • smoke_topic_model.py · sub-skills/topic-modeling/scripts/
  • SKILL.md · sub-skills/vectorizers-ctfidf/
  • api-reference.md · sub-skills/vectorizers-ctfidf/references/
  • troubleshooting.md · sub-skills/vectorizers-ctfidf/references/
  • workflows.md · sub-skills/vectorizers-ctfidf/references/
  • smoke_vectorizers.py · sub-skills/vectorizers-ctfidf/scripts/

SKILL.md (first lines)

# BERTopic

BERTopic turns documents, precomputed embeddings, or multimodal inputs into topic models you can fit, inspect, label, visualize, and save.

## Install

```bash
python -m pip install bertopic
```

Use only the optional packages that the chosen workflow needs. For example, multimodal image workflows use `bertopic[vision]`, while label and backend workflows may require `openai`, `litellm`, `langchain`, `llama-cpp-python`, `spacy`, `fastembed`, `model2vec`, `gensim`, `flair`, `safetensors`, or `datamapplot`.

If you are working from a local checkout to inspect the package, editable install is also fine:

```bash
python -m pip install -e .
```

## Quick check

Run the bundled environment check first:

```bash
python scripts/check_env.py
```

Add `--smoke` for a tiny no-download fit/load-style smoke that uses synthetic documents and precomputed embeddings.

## Route map

- `sub-skills/topic-modeling/` — build BERTopic models, fit and transform data, run `partial_fit`, mutate topics, and combine or reduce fitted models.
- `sub-skills/embeddings-backends/` — choose embedding backends, build custom embedders, inventory optional backend imports, and handle precomputed or multimodal embeddings.
- `sub-skills/vectorizers-ctfidf/` — tune `ClassTfidfTransformer`, `CountVectorizer`, and `OnlineCountVectorizer` for better topic words.
- `sub-skills/representations-labeling/` — rerank keywords, generate labels, chain representation models, and manage multi-aspect topic outputs.
- `sub-skills/analysis-visualization/` — inspect fitted models with topic tables, hierarchies, distributions, and plots.
- `sub-skills/serialization/` — save, reload, and share fitted models locally or through the Hugging Face Hub.

When a task spans more than one route, start with the earliest route in the pipeline and move forward: embeddings → model building → topic-word tuning → labels → analysis → serialization.

## Read next

- `references/workflows.md` for the fastest route through common BERTopic tasks.
- `references/troubleshooting.md` when imports, optional dependencies, plotting, or save/load fail.
- `references/repo-provenance.md` before deciding whether this skill matches the current checkout.

From VectorSpaceLab/AREX-Skill, MIT.

More from VectorSpaceLab/AREX-Skill

Signals

Stars
341
Forks
26
Open issues
0
Last commit (this folder)
Aug 26, 2026 (2 months ago)
Last release
Sep 2, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

bertopic skill. Agent Mart. https://agentmart.dev/skills/vectorspacelab-bertopic (last updated 2026-10-24)