Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Sentiment Radar
Multi-platform sentiment monitoring and analysis for products/brands/topics. Collect public opinions from Chinese platforms (小红书/XHS via MediaCrawler) and En...
skill openclawclawhub Free
Multi-platform sentiment monitoring and analysis for products/brands/topics. Collect public opinions from Chinese platforms (小红书/XHS via MediaCrawler) and En...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/analyze.py, scripts/xhs_crawler.py, scripts/dy_scrape.py, references/report-template.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Danielwangyy
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Sentiment Radar
Multi-platform social media sentiment collection and analysis.
Supported Platforms
PlatformMethodAuth Required小红书 (XHS)MediaCrawler (CDP browser)QR code loginTwitterXpoz MCP (xpoz.getTwitterPostsByKeywords)OAuth tokenRedditXpoz MCP (xpoz.getRedditPostsByKeywords)OAuth token
MediaCrawler (for 小红书)
If not installed: git clone https://github.com/NanmiCoder/MediaCrawler ~/.openclaw/workspace/skills/media-crawler cd ~/.openclaw/workspace/skills/media-crawler uv sync playwright install chromium Config: config/base_config.py — set ENABLE_CDP_MODE = True, SAVE_DATA_OPTION = "json"
Xpoz MCP (for Twitter/Reddit)
Requires mcporter with Xpoz OAuth configured. Token at ~/.mcporter/xpoz/tokens.json.
Step 1: Define targets
Identify products/brands and search keywords. Example: Products: Plaud录音笔, 钉钉闪记, 飞书录音豆 Keywords (XHS): Plaud录音笔,钉钉闪记,飞书妙记,AI录音笔评测,录音豆 Keywords (Twitter): Plaud NotePin, DingTalk recorder, Lark voice
Step 2: Collect data
XHS collection Run MediaCrawler with keywords. Use CDP mode (user's Chrome browser) for anti-detection. The crawler needs QR code scan for login — run in background with exec(background=true). cd skills/media-crawler # Update keywords in config/base_config.py, then: .venv/bin/python main.py --platform xhs --lt qrcode Environment fixes for macOS: export MPLBACKEND=Agg export PATH="/usr/sbin:$PATH" Data output: data/xhs/json/search_contents_YYYY-MM-DD.json and search_comments_YYYY-MM-DD.json Twitter/Reddit collection Use Xpoz MCP tools directly: xpoz.getTwitterPostsByKeywords — returns posts with engagement metrics xpoz.getRedditPostsByKeywords — returns posts with comments
Step 3: Analyze
Run the analysis script on collected data: python3 scripts/analyze.py \ --data ./data \ --products '{"Plaud": ["plaud","notepin"], "钉钉": ["钉钉","dingtalk","闪记"]}' \ --output report.md The script performs: Keyword distribution analysis (notes per keyword, total likes/collects) Product mention frequency in comments Sentiment classification (positive/negative/concern/neutral) Top notes ranking by engagement Price/subscription complaint extraction Product comparison comment extraction
Step 4: Report
The analysis outputs: JSON results to stdout (for programmatic use) Markdown report to --output path Combine XHS + Twitter data into a comprehensive report. See references/report-template.md for structure.
Key Analysis Dimensions
Sentiment split — positive vs negative vs concern ratio Product mentions — which products get discussed most Pricing complaints — subscription fatigue, value perception Comparison comments — head-to-head user opinions User pain points — feature requests, complaints, unmet needs Engagement metrics — likes, collects, shares as popularity signals
Notes
XHS data uses Chinese number format (e.g., "1.1万") — parse_count() in analyze.py handles this MediaCrawler has 2s sleep between requests to avoid rate limiting Each keyword returns ~20 notes per page (configurable in MediaCrawler config) Comments are fetched per note automatically For recurring monitoring, schedule via cron and compare against previous reports
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs
- SKILL.md
- references/report-template.md
- scripts/analyze.py
- scripts/dy_scrape.py
- scripts/xhs_crawler.py