Openclaw Skills turns social media comments into word frequency stats and a word cloud so you can spot trending topics in seconds.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install comment-wordcloud
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install comment-wordcloud using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Comment Wordcloud Generator?
Comment Wordcloud Generator is an Openclaw Skills workflow for converting raw social media comment JSON into actionable topic signals. It reads each item's content field, segments text with jieba, filters stop words, and calculates word frequencies so teams can quickly see what people mention most.
The skill then exports a word frequency report and a word cloud PNG, giving you both machine-readable and presentation-ready output. It is ideal for Openclaw Skills users who need fast, repeatable comment analysis without building a custom NLP pipeline.
Comment Wordcloud Generator Use Cases
- Detect hot topics across social media comments and community replies.
- Summarize audience feedback from product reviews, campaign threads, or live-event chat.
- Generate a visual word cloud for reports, presentations, or dashboards.
- Clean noisy comment data with stop words and domain-specific custom vocabulary.
- Build a repeatable Openclaw Skills routine for social listening and post-campaign analysis.
How Comment Wordcloud Generator Works
- Provide a JSON file of comments, where each record includes a
contentfield. - The script reads the dataset and parses each comment into analyzable text.
- It uses jieba to segment Chinese text and applies any custom vocabulary with higher priority.
- Stop words are removed so filler terms do not distort the results.
- The remaining tokens are counted and ranked by frequency.
- A word cloud is rendered from the top terms, limited by the configured
max_wordsvalue. - The workflow writes
{output_dir}/word_freq.jsonfor structured analysis and{output_dir}/word_cloud.pngfor visualization.
Comment Wordcloud Generator Setup
- Install Python 3.11 or later.
- Install the required libraries used by this Openclaw Skills workflow:
pip install jieba==0.42.1 wordcloud==1.9.3 matplotlib==3.9.0
- Prepare your input data as a JSON array of objects with a
contentfield. - If needed, create a stop-words text file with one word per line.
- If your domain uses special terms, define custom words so jieba can recognize them with higher priority.
- Make sure your environment has a Chinese-capable font available; the system font is used by default.
- Run the generator against your dataset:
python scripts/generate_wordcloud.py references/sample_comments.json output/ --max-words 10
Comment Wordcloud Generator Data Schema & Taxonomy
Input schema
| Field | Type | Required | Description |
|---|---|---|---|
content |
string | Yes | Raw comment text to segment, filter, and analyze. |
| array item | object | Yes | Each social media comment is represented as one object inside a JSON array. |
Runtime parameters
| Parameter | Type | Required | Purpose |
|---|---|---|---|
data_source |
file path | Yes | Path to the comment JSON file. |
output_dir |
directory path | Yes | Destination folder for generated artifacts. |
stop_words |
file path | No | Text file with one stop word per line. |
custom_words |
string | No | Comma-separated custom vocabulary added to the tokenizer. |
max_words |
integer | No | Maximum number of words rendered in the word cloud. |
Generated artifacts
| File | Contents |
|---|---|
{output_dir}/word_freq.json |
Word frequency statistics derived from segmented comment text. |
{output_dir}/word_cloud.png |
Visual word cloud image built from the highest-frequency terms. |
Metadata taxonomy
- Source metadata:
data_source, comment array structure, and rawcontentfields. - Text-processing metadata: stop words, custom words, jieba segmentation, and token normalization.
- Scoring metadata: frequency counts and term ranking.
- Visualization metadata:
max_wordscap, font availability, and PNG rendering output. - Openclaw Skills output contract: one structured JSON report plus one shareable image asset.
Comment Wordcloud Generator Advanced Features
- jieba-based segmentation improves token quality for Chinese comment analysis.
- Stop-word filtering removes filler terms and reduces visual noise.
- Custom vocabulary injection lets you prioritize brand, product, or campaign terms.
- Configurable
max_wordsgives you control over word cloud density and readability. - Dual outputs support both analytics workflows (
word_freq.json) and presentation workflows (word_cloud.png). - Designed for repeatable Openclaw Skills automation on social media comment datasets.
SKILL.md
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