Jetbrains Marketplace Reviews

JetBrains Marketplace Reviews Skill

Fetch, analyze, and visualize reviews for any plugin on the JetBrains Marketplace.

Well-Known Plugin IDs

To find the ID of any plugin, open its JetBrains Marketplace page — the numeric ID is in the URL: https://plugins.jetbrains.com/plugin/{ID}-{slug}

Workflow

1. Identify the plugin: Determine the plugin ID from user input (default: 17718 for GitHub Copilot)
2. Determine time scope: Parse user input for the desired time range (e.g. "last 2 weeks", "all reviews", "since 2025-06-01")
3. Fetch reviews: Run the fetch script with the appropriate flags
4. Visualize: Run the visualize script against the fetched JSON
5. Report: Present summary statistics and the generated chart to the user

Email Reports

When the user requests an email report or wants charts embedded in HTML:

1. Fetch reviews as usual
2. Render email charts: Use render_email_charts.py to generate an HTML report with base64-encoded PNG chart images. Email clients strip <script> tags, so JavaScript-based charting (Chart.js, Plotly) cannot work. This script uses matplotlib to render charts server-side and embeds them as <img src="data:image/png;base64,..."> — universally supported by email clients.
3. Send: Pass the HTML output as the email body via the send-email skill

Alternatively, use visualize_reviews.py --base64 to get individual chart images as a JSON dict for custom HTML assembly.

Reference Scripts

The scripts/ directory contains reference implementations that the AI agent should invoke directly. The scripts accept CLI arguments so the agent can adapt them to any user request without modifying the source.

fetch_reviews.py

Fetches reviews from the JetBrains Marketplace API with full pagination support. Works with any plugin ID.

# Default: GitHub Copilot, last 3 weeks
py .github/skills/jetbrains-marketplace-reviews/scripts/fetch_reviews.py \
  --output /path/to/reviews.json

# Specific plugin, last N weeks
py .github/skills/jetbrains-marketplace-reviews/scripts/fetch_reviews.py \
  --plugin-id 22282 --plugin-slug ai-assistant \
  --weeks 8 --output /path/to/reviews.json

# All reviews from the beginning
py .github/skills/jetbrains-marketplace-reviews/scripts/fetch_reviews.py \
  --plugin-id 17718 --all --output /path/to/reviews.json

# Date range with full content
py .github/skills/jetbrains-marketplace-reviews/scripts/fetch_reviews.py \
  --since 2025-01-01 --until 2025-06-30 --full-content \
  --output /path/to/reviews.json

CLI options:

visualize_reviews.py

Generates visualization charts from the JSON data. Auto-adapts layout and granularity based on the time span of the data:

py .github/skills/jetbrains-marketplace-reviews/scripts/visualize_reviews.py \
  --input /path/to/reviews.json --output /path/to/chart.png \
  --title "My Plugin" --no-show

CLI options:

When --base64 is used, the output is a JSON file (same path but .json extension) containing a dict of {chart_name: base64_png_string} pairs. Use these to build custom HTML emails with inline <img> tags.

render_email_charts.py

Renders all review charts as base64-encoded PNG images and outputs a complete HTML report fragment suitable for embedding in email bodies. This is the recommended approach for email reports since email clients strip <script> tags (making Chart.js/Plotly unusable).

# Generate email-ready HTML report
py .github/skills/jetbrains-marketplace-reviews/scripts/render_email_charts.py \
  --input /path/to/reviews.json --output /path/to/report.html \
  --title "GitHub Copilot"

# Also export individual charts as JSON for custom use
py .github/skills/jetbrains-marketplace-reviews/scripts/render_email_charts.py \
  --input /path/to/reviews.json --output /path/to/report.html \
  --title "GitHub Copilot" --json-output /path/to/charts.json

CLI options:

Charts included:

• Rating Distribution (bar chart)
• Average Rating by Year (bar chart, long-range only)
• Monthly Review Volume (bar chart)
• Monthly Average Rating Trend (line chart with 5-bucket moving avg + neutral line)
• Monthly Rating Breakdown (stacked bar)
• Rolling Average Rating (area chart, when enough data)
• Yearly Summary table, KPI cards, recent feedback tables

The output HTML uses inline styles and <img src="data:image/png;base64,..."> tags, which are universally supported by email clients (Outlook, Gmail, Apple Mail, etc.).

API Details

GET https://plugins.jetbrains.com/api/plugins/{plugin_id}/comments?size=100&page={page}

• API returns max 100 reviews per page; use page=1,2,3… for pagination
• Reviews are sorted newest-first

JSON Data Schema

Each review object in the output JSON:

{
  "id": 134243,
  "date": "2026-02-10",
  "rating": 5,
  "has_replies": false,
  "link": "https://plugins.jetbrains.com/plugin/17718-.../reviews#review=134243",
  "author": "Enes Oscar",
  "comment": "Overall, it provides a great experience..."
}

• rating: 0 = no rating given, 1-5 = star rating
• author and comment are only present when --full-content is used

Usage Examples

• "Get the last 2 weeks of JetBrains Copilot reviews" → --plugin-id 17718 --weeks 2
• "Fetch all Copilot JetBrains reviews ever" → --plugin-id 17718 --all
• "Show reviews for JetBrains AI Assistant from January 2025" → --plugin-id 22282 --plugin-slug ai-assistant --since 2025-01-01 --until 2025-01-31
• "Visualize the rating trend for the last 6 months" → --weeks 26
• "Analyze recent feedback with full review text" → --weeks 3 --full-content

Dependencies

• Python 3.x (stdlib only for fetching)
• matplotlib, numpy (for visualization)
• scipy (optional, for trend smoothing)

Install: py -m pip install matplotlib numpy scipy

Notes

• Reviews with rating: 0 mean the user left a comment without giving a star rating
• The API rate-limits large batch requests; the fetch script paginates responsibly
• For long ranges, the visualize script automatically switches to monthly aggregation and a 6-panel layout with yearly summaries, stacked breakdowns, and rolling averages