Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets
Quickly install sentiment-analyzer AI skill to your development environment via command line
Source: guia-matthieu/clawfu-skills.
Analyze sentiment in customer feedback using transformer models - understand what your customers really feel at scale.
| Structures analysis frameworks | Metric definitions | | Identifies patterns in data | Business interpretation | | Creates visualization templates | Dashboard design | | Suggests optimization areas | Action priorities | | Calculates statistical measures | Decision thresholds |
| 0.8 - 1.0 | Very Positive | Enthusiastic, recommend | | 0.6 - 0.8 | Positive | Satisfied, happy | | 0.4 - 0.6 | Neutral | Mixed or indifferent | | 0.2 - 0.4 | Negative | Disappointed, frustrated | | 0.0 - 0.2 | Very Negative | Angry, will churn |
Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets Source: guia-matthieu/clawfu-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill sentiment-analyzerAnalyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets Source: guia-matthieu/clawfu-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/guia-matthieu/clawfu-skills --skill sentiment-analyzer Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/guia-matthieu/clawfu-skills