pkking/sentiment-analysis
dotfiles/dot_config/skillshare/skills/sentiment-analysis/SKILL.md
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.
data-ai
Updated Apr 27, 2026
$ install --global
skillsauthnpx skillsauth add pkking/dotfiles sentiment-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 27, 2026, 9:49 AM23.5s2 files scanned
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)
- 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
- Market Research: Advanced Techniques
- User Interviews: The Ultimate Guide to Research Interviews
Related Skills
pkking/wizard
databases
VerifiedTrustedCommunity
Generate an interactive bash wizard that walks a human through a manual procedure — third-party setup, a one-off migration, an A→B state transition — opening URLs, capturing values, confirming each step, and writing .env files and GitHub Actions secrets.
SKILL.mdUpdated Jul 29, 2026
pkking/to-questionnaire
testing
VerifiedTrustedCommunity
Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
SKILL.mdUpdated Jul 29, 2026
pkking/setup-ts-deep-modules
development
VerifiedTrustedCommunity
Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.
SKILL.mdUpdated Jul 29, 2026
pkking/loop-me
development
VerifiedTrustedCommunity
Grill me about specs for the workflows I want to build, within this workspace.
SKILL.mdUpdated Jul 29, 2026