Vibe Check
ListedAnalyzes text to gauge sentiment and emotional tone – useful for understanding user reactions and feedback quickly.
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vibe-check. vibiumdev/vibium has no skill named "vibe-check" any more. It was renamed, moved or deleted upstream.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-30. We don't show a command we know will fail.
What we know about this skill
Publisher
vibiumdev
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Install target
Live repository
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About this skill
What it does
The vibe-check skill allows AI agents to assess the emotional tone, social atmosphere, and general "feel" of a conversation or situation. It helps determine if interactions are positive, tense, or neutral before proceeding with further dialogue or actions.
When to use it
- Conflict Resolution: Evaluate the tension level in a customer support chat to decide whether to de-escalate or escalate to a human agent.
- Social Bot Interaction: Ensure a companion bot responds with appropriate empathy when a user expresses sadness or frustration.
- Meeting Analysis: Scan transcribed meeting notes to gauge team morale and identify potential friction points before drafting summaries.
- Content Moderation: Detect subtle toxicity or aggression in comments that might not trigger standard keyword filters.
Key capabilities
- Analyzes emotional tone and sentiment beyond simple positive/negative binary classifications.
- Assesses the social atmosphere and interpersonal dynamics within a text.
- Provides a "vibe score" or qualitative assessment of the current interaction state.
Example prompts
- "Run a vibe-check on this customer email to see if they are angry or just frustrated."
- "Analyze the chat history between User A and User B; what is the current social vibe?"
- "Before I send this draft message, do a vibe-check to ensure it sounds friendly and not robotic."
Tips & gotchas
- Context Matters: The skill relies heavily on surrounding text; isolated sentences may yield inaccurate emotional assessments.
- Nuance Detection: It is particularly useful for detecting sarcasm or passive-aggression that standard sentiment analysis often misses.