v1.0
This commit is contained in:
Pawel Huryn
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---
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description: Analyze user feedback at scale — sentiment analysis, theme extraction, and segment-level insights
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argument-hint: "<feedback data as CSV, text, or file>"
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---
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# /analyze-feedback -- User Feedback Analysis
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Process large volumes of user feedback (reviews, surveys, support tickets, NPS responses) into structured insights with sentiment analysis and segment-level patterns.
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## Invocation
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```
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/analyze-feedback [upload a CSV of NPS responses]
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/analyze-feedback [paste app store reviews or survey responses]
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/analyze-feedback [upload support ticket export]
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```
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## Workflow
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### Step 1: Accept Feedback Data
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Accept in any format:
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- CSV/Excel with feedback text (and optional metadata: date, segment, rating)
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- Pasted text (reviews, survey responses, Slack messages)
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- Uploaded documents or exports from feedback tools
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Ask:
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- What kind of feedback is this? (NPS, reviews, support tickets, survey, etc.)
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- Any segments to analyze separately? (user tier, plan, geography)
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- What are you looking for? (general themes, specific issues, trends over time)
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### Step 2: Analyze
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Apply the **sentiment-analysis** skill:
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- **Sentiment scoring**: Classify each piece of feedback (positive, neutral, negative)
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- **Theme extraction**: Identify recurring topics and cluster related feedback
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- **Frequency analysis**: Count how often each theme appears
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- **Segment analysis**: Break down sentiment and themes by user segment (if data available)
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- **Trend detection**: If dates are available, identify sentiment shifts over time
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### Step 3: Generate Analysis Report
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```
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## Feedback Analysis Report
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**Date**: [today]
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**Feedback analyzed**: [count] responses
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**Source**: [NPS survey / app reviews / support tickets / etc.]
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**Period**: [date range if available]
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### Overall Sentiment
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- Positive: [X%] | Neutral: [Y%] | Negative: [Z%]
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- Average sentiment score: [X/10]
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- Trend: [improving / stable / declining]
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### Top Themes
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| # | Theme | Mentions | Sentiment | Segments Most Affected |
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|---|-------|----------|-----------|----------------------|
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### Theme Deep-Dive
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#### Theme 1: [Name] — [X] mentions, [sentiment]
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- **What users are saying**: [summary with representative quotes]
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- **Root cause**: [what's driving this feedback]
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- **Impact**: [how this affects retention, satisfaction, or revenue]
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- **Recommendation**: [what to do about it]
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[Repeat for top 5-8 themes]
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### Segment Analysis
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| Segment | Volume | Avg Sentiment | Top Theme | Key Difference |
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|---------|--------|-------------|-----------|---------------|
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### Notable Quotes
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> "[quote]" — [segment, sentiment]
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### Trends Over Time
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[If date data available: chart-ready data showing sentiment shifts]
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### Actionable Insights
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1. [Insight + recommended action]
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2. ...
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### Gaps
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[What this feedback doesn't tell you — suggested follow-up research]
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```
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Save as markdown. If input was structured data (CSV), also save enriched data with sentiment scores as CSV.
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### Step 4: Offer Next Steps
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- "Want me to **create user personas** from these feedback patterns?"
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- "Should I **triage the top themes as feature requests**?"
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- "Want me to **design an interview script** to go deeper on a specific theme?"
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## Notes
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- Sentiment analysis is approximate — flag edge cases (sarcasm, mixed sentiment, non-English text)
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- Theme extraction should look for needs behind requests, not just surface-level topics
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- If sample sizes are small per segment, note limited confidence
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- For NPS data specifically, analyze Detractors (0-6), Passives (7-8), and Promoters (9-10) separately
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- Output enriched CSV when input is structured, so the user can use it in their own tools
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---
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description: Analyze the competitive landscape — identify competitors, compare strengths and weaknesses, find differentiation opportunities
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argument-hint: "<your product or market>"
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---
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# /competitive-analysis -- Competitive Landscape Analysis
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Research and analyze your competitive landscape. Identifies direct and indirect competitors, maps positioning, and surfaces differentiation opportunities.
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## Invocation
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```
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/competitive-analysis AI-powered project management tools
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/competitive-analysis Our product vs Notion, Asana, and Monday.com
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/competitive-analysis [upload a competitor list or market brief]
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```
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## Workflow
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### Step 1: Understand the Competitive Context
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Ask:
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- What is your product? What category does it compete in?
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- Any specific competitors you want analyzed? Or should I identify them?
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- What's the lens? (feature comparison, positioning, pricing, go-to-market)
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- What will you use this analysis for? (strategy, sales enablement, investor pitch, product roadmap)
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### Step 2: Identify Competitors
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Apply the **competitor-analysis** skill:
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- Identify 5 direct competitors (same category, same buyer)
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- Identify 2-3 indirect competitors (different approach, same job-to-be-done)
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- Note emerging/disruptive players if relevant
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- Use web research to gather current information
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### Step 3: Analyze Each Competitor
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For each competitor:
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- **Positioning**: How they describe themselves, target audience, key messaging
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- **Strengths**: What they do well, where they win
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- **Weaknesses**: Where they fall short, common complaints
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- **Pricing**: Model and price points (if public)
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- **Market traction**: Funding, team size, customer base signals
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- **Recent moves**: New features, partnerships, pivots
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### Step 4: Generate Competitive Analysis
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```
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## Competitive Analysis: [Your Product/Market]
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**Date**: [today]
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**Analyzed**: [count] competitors
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### Market Overview
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[2-3 sentences on market dynamics, trends, and where it's heading]
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### Competitive Landscape
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| Competitor | Category | Target | Positioning | Strength | Weakness |
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|-----------|----------|--------|------------|----------|----------|
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### Feature Comparison Matrix
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| Capability | Your Product | Competitor A | Competitor B | Competitor C |
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|-----------|-------------|-------------|-------------|-------------|
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### Positioning Map
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[2x2 matrix showing competitive positioning on key dimensions]
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### Differentiation Opportunities
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1. **[Opportunity]** — [why it's defensible and valuable]
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2. ...
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### Competitive Threats
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1. **[Threat]** — [what to watch for, recommended response]
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2. ...
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### Recommendations
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- **Double down on**: [your unique advantages]
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- **Close the gap on**: [table-stakes features you're missing]
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- **Ignore**: [competitor moves that aren't worth responding to]
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```
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Save as markdown.
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### Step 5: Offer Next Steps
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- "Want me to **create a battlecard** for sales against a specific competitor?"
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- "Should I **develop positioning** that differentiates from the top competitors?"
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- "Want me to **identify feature gaps** to close and add to the roadmap?"
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## Notes
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- Web research is used for current competitor data — results are as fresh as available sources
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- Distinguish between "table stakes" (must-have to compete) and "differentiators" (must-have to win)
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- Don't just list features — analyze *why* competitors make the choices they make
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- Pricing intelligence should note whether pricing is public, usage-based, or requires sales contact
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- Update this analysis quarterly — competitive landscapes shift fast
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---
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description: Comprehensive user research — build personas, segment users, and map the customer journey from research data
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argument-hint: "<research data, survey results, or product description>"
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---
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# /research-users -- User Research Synthesis
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Turn raw research data into actionable user personas, behavioral segments, and customer journey maps. Accepts survey data, interview notes, feedback, analytics, or a product description for exploratory research.
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## Invocation
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```
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/research-users [upload survey results, interview notes, or feedback data]
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/research-users B2B project management tool for agencies — help me understand our users
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/research-users [paste user feedback or support ticket data]
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```
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## Workflow
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### Step 1: Accept Research Inputs
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Accept from any combination:
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- Survey responses (CSV, spreadsheet, pasted)
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- Interview notes or transcripts
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- Support tickets or feature requests
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- Product analytics / behavioral data
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- NPS or satisfaction data
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- Product description (for exploratory research without data)
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Ask:
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- What research do you have? What format?
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- What do you want to understand? (who are our users, how do they differ, where's the friction)
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- What decisions will this inform? (roadmap, positioning, pricing, onboarding)
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### Step 2: Build Personas
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Apply the **user-personas** skill:
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- Identify 3-4 distinct personas from the data
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- For each persona: name, role, goals (JTBD), pains, gains, behavioral patterns
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- Include unexpected insights — things that surprised you in the data
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- Note persona prevalence (what % of your base each represents, if data allows)
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### Step 3: Segment Users
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Apply the **user-segmentation** and **market-segments** skills:
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- Create behavioral segments (not just demographics)
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- For each segment: size, JTBD, product fit, willingness to pay, engagement level
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- Identify the highest-value segment and the highest-growth segment
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- Map segments to personas (how they overlap)
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### Step 4: Map the Customer Journey
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Apply the **customer-journey-map** skill:
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- Map the end-to-end journey: Awareness → Consideration → Onboarding → Active Use → Expansion → Advocacy
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- For each stage: touchpoints, emotions, pain points, aha moments
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- Identify the biggest drop-off points
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- Highlight moments of delight worth amplifying
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### Step 5: Generate Research Report
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```
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## User Research Report: [Product]
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**Date**: [today]
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**Data sources**: [what was analyzed]
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**Sample size**: [if applicable]
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### Executive Summary
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[3-5 sentences: key findings and implications]
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### Personas
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#### Persona 1: [Name] — "[Quote that captures them]"
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- **Who**: [role, context, experience level]
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- **Primary JTBD**: [When..., I want to..., so I can...]
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- **Key pains**: [top 3]
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- **Key gains**: [what delights them]
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- **Behavioral pattern**: [how they use the product]
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- **Prevalence**: [X% of user base]
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[Repeat for each persona]
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### User Segments
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| Segment | Size | Primary JTBD | Product Fit | Value | Growth |
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|---------|------|-------------|-------------|-------|--------|
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### Customer Journey Map
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| Stage | Touchpoints | Emotion | Pain Points | Opportunities |
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|-------|------------|---------|-------------|---------------|
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### Key Insights
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1. [Insight with supporting evidence]
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2. ...
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### Recommendations
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1. [Actionable recommendation tied to findings]
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2. ...
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### Open Questions
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[What the data didn't answer — suggested follow-up research]
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```
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Save as markdown.
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### Step 6: Offer Next Steps
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- "Want me to **create interview scripts** to go deeper on a specific persona?"
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- "Should I **analyze sentiment** across these segments?"
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- "Want me to **build a value proposition** for the top persona?"
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- "Should I **prioritize the journey map pain points** as feature opportunities?"
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## Notes
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- If data is thin, be transparent about confidence levels — 5 interviews → hypotheses, not conclusions
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- Personas should be useful, not decorative — every persona should influence a product decision
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- Behavioral segments are more actionable than demographic segments for product decisions
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- The journey map should surface emotions, not just actions — where users feel frustrated vs. delighted drives prioritization
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- If no data is provided, generate research-informed hypotheses and recommend how to validate them
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