sentiment-analysis▌
phuryn/pm-skills · updated Apr 8, 2026
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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.
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)
- Data Ingestion: Read all feedback sources and create a working inventory
- Segment Identification: Identify at least 3 distinct user segments or personas from the feedback
- Thematic Analysis: Extract recurring themes, pain points, and positive feedback per segment
- Sentiment Scoring: Assign sentiment scores (-1 to +1) for overall satisfaction per segment
- Impact Assessment: Prioritize insights by frequency, severity, and business impact
- 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
How to use sentiment-analysis on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add sentiment-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sentiment-analysis from GitHub repository phuryn/pm-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sentiment-analysis. Access the skill through slash commands (e.g., /sentiment-analysis) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★25 reviews- ★★★★★Noor Bhatia· Dec 20, 2024
sentiment-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dev Liu· Sep 25, 2024
sentiment-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Rahul Santra· Sep 21, 2024
sentiment-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakshi Patil· Sep 13, 2024
Registry listing for sentiment-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arya Li· Aug 16, 2024
Registry listing for sentiment-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Aug 12, 2024
Keeps context tight: sentiment-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Aug 4, 2024
sentiment-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hassan Johnson· Aug 4, 2024
sentiment-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Piyush G· Jul 23, 2024
I recommend sentiment-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★William Khanna· Jul 7, 2024
Useful defaults in sentiment-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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