Israeli Chatbot Analytics
Verified94/100Analyze and optimize Hebrew chatbot performance with conversation flow analytics, Hebrew sentiment analysis, drop-off detection, user satisfaction scoring, A/B testing for response variants, and reporting dashboards.
Trust score 94/100 (Verified) · 23+ installs · 3 GitHub contributors · MIT license
Teams deploy Hebrew chatbots without proper analytics, making it impossible to know if the bot actually helps users or where conversations break down. The challenge is amplified by Hebrew-specific issues like sentiment analysis accuracy and RTL visualization.
skills-ilnpx skills-il add skills-il/developer-tools --skill israeli-chatbot-analytics -a claude-codeInstall on Claude.ai, Claude Desktop, ChatGPT, Manus, or other platforms
- 1. Click "Download ZIP" to download the skill files.
- 2. Open Claude Desktop and go to Customize > Skills.
- 3. Click "+" and select "Upload a skill", then upload the ZIP file.
- 4. Start a new conversation. The skill will activate automatically when relevant.
When to Apply
- When analyzing why users abandon chatbot conversations
- When measuring Hebrew chatbot sentiment accuracy
- When A/B testing different Hebrew response phrasings
- When building performance dashboards for a chatbot team
- When setting up alerts for chatbot performance degradation
Try These Prompts
Analyze the chatbot conversation logs and identify drop-off points where users abandon the conversation. Break down by: question type, conversation stage, time of day, and device. Present findings in a table with drop-off percentage per point and improvement recommendations.
Set up a real-time sentiment tracking system for Hebrew chatbot conversations. The system should detect sentiment shifts during the conversation, handle Israeli slang and sarcasm, and flag conversations where sentiment drops below a defined threshold.
Create a weekly chatbot performance report template that includes: conversation count, resolution rate, average response time, average sentiment, top 10 unanswered questions, and week-over-week comparison. The report should support Hebrew with RTL-compatible charts.
Set up an automatic alert system that detects anomalies in chatbot performance: sharp increase in abandonment, drop in average sentiment, unusual response times, or rise in unanswered questions. Define thresholds for each metric and alert channels (Slack, email).
Frequently Asked Questions
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