Conversation Analyzer

The conversation-analyzer skill provides a structured framework for dissecting dialogue interactions, breaking them down into distinct components like intent, sentiment, and context. It enables AI agents to move beyond simple text matching by offering deep insights into the underlying dynamics of a conversation.

When to use it

• Diagnosing why an agent failed to resolve a user's issue during a specific turn.
• Evaluating the emotional tone of customer support chats to identify frustration spikes.
• Auditing training data conversations for clarity and logical flow before deployment.
• Debugging complex multi-turn interactions where context was lost or misunderstood.

Key capabilities

• Decomposes dialogue into granular structural elements.
• Identifies specific conversational intents and shifts in topic.
• Analyzes sentiment trajectory throughout the exchange.
• Highlights gaps in context or missing information within the flow.

Example prompts

• "Analyze this customer support transcript to identify where the agent lost track of the user's primary goal."
• "Break down the following dialogue into intent, sentiment, and context segments for review."
• "Use conversation-analyzer to find specific turns in this log where the tone became negative."

Tips & gotchas

Ensure you provide a complete transcript or clearly defined turn boundaries, as fragmented input may yield incomplete structural analysis. This skill is best used when you need actionable diagnostic data rather than just a summary of what was said.