An AI-powered Bilibili comment analysis tool that fetches top-level comments, performs 4D sentiment analysis, and generates interactive dark-mode HTML reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bilibili-comment
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 bilibili-comment 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 Bilibili Comment Analyzer?
The Bilibili Comment Analyzer is a professional AI coding agent extension designed for content creators, brand managers, and digital marketers who want to extract intelligence from audience feedback on Bilibili. By simply inputting a Bilibili video link or BV ID, this tool fetches top-level comment data, understands complex Bilibili slang and meme culture (such as 'yyds', 'awsl', and 'xswl'), and performs deep four-dimensional AI sentiment analysis (Positive, Negative, Needs, and Competitors).
As a standout addition to the library of Openclaw Skills, this tool automates the tedious task of manual review by organizing comments into interactive, dark-theme HTML reports. Developers, UP hosts, and researchers can run this lightweight tool with zero extra Python dependencies, allowing them to rapidly extract insights, track brand public opinion, and optimize content strategy.
Bilibili Comment Analyzer Use Cases
- Content Strategy Review: Content creators and UP hosts can analyze audience comments to discover which parts of a video resonated most, optimizing future topic selection and video formats.
- Brand Public Opinion Monitoring: Brand operators can quickly analyze negative feedback or emerging crises on promotional videos to formulate timely response strategies.
- Competitor Analysis: Content planners can analyze user comments on competitors' videos to identify competitor strengths, weaknesses, and unique market differentiation.
- User Needs Mining: Product managers and researchers can extract direct user requests and pain points from Bilibili video comment sections to guide product iteration.
How Bilibili Comment Analyzer Works
- Input Extraction: The user provides a Bilibili video URL or BV ID in natural language, which the AI agent automatically extracts.
- Comment Retrieval: The tool calls the Redfox API (via
bili_comment_search.py) using your configured Redfox API key to fetch top-level comments (20 comments per page) along with video metadata (views, likes, coins, bookmarks, and danmaku). - Initial HTML Generation: A beautiful dark-theme interactive HTML report is generated from a template, containing raw comment data and placeholders for AI analysis.
- AI 4D Sentiment Analysis: The AI analyzes the comments across four dimensions: Positive, Negative, User Needs, and Competitors, capturing localized memes and slang accurately.
- Secure Backfilling: The AI analysis results are securely written into the HTML report via
backfill_html.py, preserving UTF-8 encoding to prevent Chinese character corruption.
Bilibili Comment Analyzer Setup
Setting up this tool within your Openclaw Skills workspace is straightforward. It relies entirely on the Python standard library with no external dependencies.
1. Get an API Key
Register and obtain an API Key from Redfox Hub.
2. Configure Your Environment
You can configure the API Key in one of two ways:
Option A: Global Configuration File
Add your key to ~/.openclaw/openclaw.json:
{
"env": {
"REDFOX_API_KEY": "ak_your_api_key_here"
}
}
Option B: Temporary Environment Variable Run the following command in your terminal:
export REDFOX_API_KEY="ak_your_api_key_here"
3. Usage Syntax
Interact with the AI agent using natural language commands. No fixed CLI commands are necessary:
- "Fetch comments for Bilibili video BV1xx411c7mD"
- "Next page" or "Page 3" (retains current video context)
- "Generate the HTML report"
Bilibili Comment Analyzer Data Schema & Taxonomy
The skill maintains a structured project directory to organize scripts, templates, and output reports cleanly:
bilibili-comment/
├── SKILL.md # Skill configuration & documentation
├── references/
│ └── core_workflow.md # Core execution workflow instructions
├── assets/
│ └── report_template.html # Interactive dark-theme HTML template
└── scripts/
├── bili_comment_search.py # Fetches comments and builds basic HTML
└── backfill_html.py # Safe AI analysis backfill utility
Output Report Metadata & Structure
The generated HTML report incorporates three main datasets organized within a clean visual hierarchy:
| Data Category | Fields Extracted | Purpose |
|---|---|---|
| Video Details | Title, UP Host, Views, Likes, Coins, Bookmarks, Danmaku | Overall performance summary |
| Comments Grid | Username, User Link, Comment Content, Likes, Timestamp, IP Location | Granular comment tracking |
| AI Analysis | Pinned Comment Flag, 4D Sentiment Metrics, Slang Decoding | Qualitative audience sentiment analysis |
Bilibili Comment Analyzer Advanced Features
- Bilibili Slang & Meme Decoder: Automatically decodes localized platform internet slang such as 'yyds', 'awsl', 'xswl', and '爷青回' to derive true sentiment indicators.
- Safe UTF-8 Encoding Safeguard: Uses a dedicated backfill utility (
backfill_html.py) to bypass traditional encoding issues, ensuring zero Chinese character corruption in the final reports. - Paginated Context Retention: The AI agent remembers the active BV ID, letting you jump to specific pages (e.g., 'Page 3' or 'Next page') without re-entering the video URL.
- Zero-Dependency Python Architecture: Constructed purely with the Python standard library (
urllib), guaranteeing seamless portability across different environments without worrying about broken dependencies.
SKILL.md
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