A high-performance query tool for retrieving real-time adult video metadata, rankings, and actress details from the FANZA GraphQL API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install avbuzz
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 avbuzz 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 AVBUZZ?
AVBUZZ is a specialized skill designed for Openclaw Skills that interfaces directly with the undocumented FANZA GraphQL API. It provides developers and AI agents with the ability to fetch rich metadata including product codes, titles, actress information, and high-resolution cover images without requiring any authentication or API keys.
The tool is specifically optimized for technical environments, utilizing JST (Japan Standard Time) to ensure accurate tracking of daily releases. It serves as a bridge for Openclaw Skills to access deep product indexes, allowing for complex searches by keyword, studio, or unique performer IDs. Whether used for data analysis or personal organization, it provides a streamlined way to interact with one of the largest media databases in Japan.
AVBUZZ Use Cases
- Fetching the latest daily new releases synchronized with JST.
- Identifying trending content through sales, review, or bookmark-based rankings.
- Performing fuzzy searches for specific actresses, studios, or video codes (番號).
- Retrieving comprehensive filmographies for specific performers using unique Actress IDs.
- Monitoring specific studios for new production entries.
How AVBUZZ Works
- The user or AI agent provides a query parameter such as a date, keyword, or ranking type.
- The skill determines the current JST time to align with the database's refresh cycle.
- A GraphQL request is constructed using the legacySearchPPV operation with defined filters for the Openclaw Skills workflow.
- The skill executes a POST request to the FANZA API endpoint using curl.
- The returned JSON payload is parsed to extract fields like product IDs, review scores, and CDN-hosted image URLs.
- Data is formatted into a clean structure for display or further processing by the AI agent.
AVBUZZ Setup
The basic skill requires curl to be installed on your system. For the standard Openclaw Skills environment, ensure curl is available in your PATH. For advanced users wishing to deploy the persistent bot component, use the following commands:
git clone https://github.com/zxcnny930/avbuzz.git
cd avbuzz
npm install
cp config.example.json config.json
# Configure your Discord or Telegram tokens in config.json
npm start
AVBUZZ Data Schema & Taxonomy
The skill organizes results from the GraphQL API into a structured format. Key data points include:
| Field | Description |
|---|---|
| id | The FANZA product code or 番號 (e.g., ssis00001). |
| title | The full Japanese title of the video. |
| deliveryStartAt | The official release date in YYYY-MM-DD format. |
| contentType | Categorization, such as TWO_DIMENSION or VR. |
| maker.name | The production studio or maker name. |
| actresses | An array of performer objects containing name and unique ID. |
| review | Metrics including average star rating (1.0-5.0) and total count. |
| bookmarkCount | The total number of users who have saved the content. |
AVBUZZ Advanced Features
- Multi-platform integration supporting both Discord slash commands and Telegram bot notifications.
- Automated daily digests sent at specific JST times to keep users updated on new releases.
- Actress Tracking system that triggers alerts immediately when a followed performer has a new release.
- Server-side filtering logic to isolate VR content from standard 2D releases.
- Advanced sorting parameters including SALES_RANK_SCORE and BOOKMARK_COUNT for market research.
- Robust error handling for rate limits (429) and GraphQL syntax validation.
SKILL.md
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