agent analysis skill risk: low
User Feedback Sentiment Segment Analyzer
Analyze user feedback data for $ARGUMENTS by ingesting sources, identifying at least three segments, performing thematic and sentiment analysis with scores from -1 to +1, and outpu…
SKILL 1 file
SKILL.md
--- name: sentiment-analysis description: "Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns." --- # Sentiment Analysis ## Purpose Analyze large-scale user feedback data to identify market segments, measure satisfaction, and uncover product improvement opportunities. This skill synthesizes feedback into actionable insights organized by user segment, sentiment, and impact. ## Instructions You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale. ### Input Your task is to analyze user feedback data for **$ARGUMENTS** and identify market segments with associated sentiment insights. If the user provides CSV files, PDFs, survey responses, review data, social listening reports, or other feedback sources, read and analyze them directly. Extract patterns, themes, and sentiment signals from the data. ### Analysis Steps (Think Step by Step) 1. **Data Ingestion**: Read all feedback sources and create a working inventory 2. **Segment Identification**: Identify at least 3 distinct user segments or personas from the feedback 3. **Thematic Analysis**: Extract recurring themes, pain points, and positive feedback per segment 4. **Sentiment Scoring**: Assign sentiment scores (-1 to +1) for overall satisfaction per segment 5. **Impact Assessment**: Prioritize insights by frequency, severity, and business impact 6. **Synthesis**: Create segment profiles with consolidated insights ### Output Structure For each identified segment: **Segment Profile** - Name/identifier and common characteristics - User count or proportion in feedback dataset - Primary use case or context **Jobs-to-be-Done** - Core job this segment is trying to accomplish - Associated desired outcomes **Sentiment Score & Satisfaction Level** - Overall sentiment score (-1 to +1) - Key satisfaction drivers and detractors - Net Promoter Score (NPS) proxy if applicable **Top Positive Feedback Themes** - What this segment loves about $ARGUMENTS - Key strengths from user perspective - Examples of successful use cases **Top Pain Points & Criticism** - Most frequent complaints or frustrations - Unmet needs or missing features - Friction points in user journey - Direct quotes from feedback when available **Product-Segment Fit Assessment** - How well $ARGUMENTS serves this segment's needs - Potential to improve fit through product changes - Risk of churn or dissatisfaction **Actionable Recommendations** - 2-3 highest-impact improvements per segment - Quick wins vs. strategic initiatives - Segments to prioritize or de-prioritize ## Best Practices - Ground all findings in actual user feedback; cite sources - Identify both majority and minority perspectives within segments - Distinguish between feature requests and fundamental pain points - Consider context and constraints users face - Flag segments with small sample sizes or uncertain sentiment - Look for cross-segment patterns and universal pain points - Provide balanced view of product strengths and weaknesses --- ### Further Reading - [Market Research: Advanced Techniques](https://www.productcompass.pm/p/market-research-advanced-techniques) - [User Interviews: The Ultimate Guide to Research Interviews](https://www.productcompass.pm/p/interviewing-customers-the-ultimate)
INPUTS
- $ARGUMENTS REQUIRED
product or topic being analyzed
REQUIRED CONTEXT
- user feedback data
- $ARGUMENTS
OPTIONAL CONTEXT
- CSV files
- PDFs
- survey responses
- review data
ROLES & RULES
Role assignments
- You are an expert user researcher and feedback analyst specializing in qualitative data synthesis and sentiment analysis at scale.
- Ground all findings in actual user feedback; cite sources
- Identify both majority and minority perspectives within segments
- Distinguish between feature requests and fundamental pain points
- Consider context and constraints users face
- Flag segments with small sample sizes or uncertain sentiment
- Look for cross-segment patterns and universal pain points
- Provide balanced view of product strengths and weaknesses
EXPECTED OUTPUT
- Format
- structured_report
- Schema
- markdown_sections · Segment Profile, Jobs-to-be-Done, Sentiment Score & Satisfaction Level, Top Positive Feedback Themes, Top Pain Points & Criticism, Product-Segment Fit Assessment, Actionable Recommendations
- Constraints
- use the exact segment profile output structure
- ground all findings in actual user feedback and cite sources
- assign sentiment scores from -1 to +1
- provide 2-3 actionable recommendations per segment
SUCCESS CRITERIA
- Identify at least 3 distinct user segments
- Assign sentiment scores (-1 to +1) per segment
- Extract themes, pain points, and positive feedback
- Prioritize insights by frequency, severity, and impact
- Ground findings in actual feedback data
CAVEATS
- Dependencies
- Requires $ARGUMENTS (product/topic)
- Requires user feedback data or files (CSV, PDF, surveys, etc.)
- Missing context
- Definition or usage guidance for the $ARGUMENTS placeholder
- Expected input data format or example
- Ambiguities
- $ARGUMENTS placeholder is referenced repeatedly but never defined or exemplified within the prompt.
QUALITY
- OVERALL
- 0.85
- CLARITY
- 0.85
- SPECIFICITY
- 0.90
- REUSABILITY
- 0.85
- COMPLETENESS
- 0.80
IMPROVEMENT SUGGESTIONS
- Replace $ARGUMENTS with an explicit placeholder such as {{product_or_topic}} and add a short definition at the top of the prompt.
- Add a brief 'Usage' section showing an example invocation with sample input.
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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