XiaoMaColtAI

XiaoMaColtAI / BettaFish-skill

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BettaFish-skill：改造自BettaFish（微舆），多Agent舆情分析助手，可以用在Claude Code、OpenClaw、Cursor等支持Skills的Agent中，更方便使用

18
1
69% credibility
Found Mar 10, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

BettaFish-skill is a set of tools for gathering real social media data, analyzing sentiments, visualizing connections, and creating professional opinion reports.

How It Works

1
🔍 Discover BettaFish

You hear about a helpful tool called BettaFish that checks what people are saying online about brands or topics.

2
💭 Choose your topic

Pick something you're curious about, like your business or a hot event, to see public opinions.

3
🌐 Gather real chatter

It pulls fresh posts and comments from social sites so you get true feelings, not made-up stuff.

4
😊 Spot moods and buzz

See if people are happy, upset, or neutral, plus which topics are heating up.

5
🗺️ Map the connections

Watch a visual web form showing how opinions link together for deeper understanding.

6
📝 Craft your report

Turn insights into a clean document ready to share or act on.

🎯 Unlock smart advice

You now have clear recommendations to boost your brand or handle issues confidently.

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AI-Generated Review

What is BettaFish-skill?

BettaFish-skill is a Python skill that turns AI agents into multi-agent public opinion analysts, pulling real-time data from Chinese social platforms like Weibo and Douyin via web search tools. It processes posts for sentiment, engagement, and trends, then builds knowledge graphs and spits out professional reports in Word, PDF, or interactive HTML. Drop it into Claude Code, Cursor, or any skill-supporting agent for instant舆情 (public sentiment) breakdowns without faking data.

Why is it gaining traction?

It mandates real web-fetched data—no simulations—making outputs trustworthy for actual PR work, unlike toy sentiment demos. The end-to-end flow from search to visualized reports with heat indexes and risk alerts saves hours versus cobbling together separate tools. Python simplicity lets agent users focus on prompts, not pipelines.

Who should use this?

PR managers scripting agent workflows for brand monitoring on Xiaohongshu or Bilibili. Devs integrating sentiment into Cursor bots for quick topic scans. Chinese market analysts needing automated reports on viral events without full data science stacks.

Verdict

Grab it if you're in agent-driven analysis for Chinese social media—solid for prototypes despite 16 stars and 0.7% credibility score signaling early days. Docs are thin and tests absent, so expect tweaks; fork and harden for production.

(187 words)

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