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Sentiment Analysis

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Research and analysis
  • Business and productivity
  • MIT
  • No issues found
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From the author

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.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as an expert user researcher, processing qualitative feedback from sources like surveys, reviews, and social listening. It systematically categorizes data into distinct user segments, assigns sentiment scores, and identifies Jobs-to-be-Done (JTBD) to provide a clear picture of product-market fit and improvement opportunities.

What it does

  • Ingests and inventories diverse user feedback sources
  • Identifies distinct user segments and personas
  • Performs thematic analysis on pain points and positive feedback
  • Calculates sentiment scores and NPS proxies per segment
  • Assesses product-segment fit and churn risk
  • Generates actionable recommendations for product improvements

When to use it

  • When you have a large volume of raw user feedback to synthesize
  • When you need to understand how different user segments perceive your product
  • When identifying high-impact product improvements based on user pain points
  • When preparing for product roadmap planning sessions

Advantages

  • Transforms unstructured feedback into structured, actionable reports
  • Provides a balanced view of both majority and minority user perspectives
  • Prioritizes insights based on business impact and severity
  • Grounds all findings in direct user feedback

Usage examples

“Analyze the attached CSV of customer survey responses for our mobile app.”
The agent will output a structured report detailing user segments, their sentiment scores, primary pain points, and specific recommendations for product updates.
“Review these 50 recent app store reviews and tell me what our power users are struggling with.”
The agent will identify the 'power user' segment, extract their specific Jobs-to-be-Done, and list the friction points preventing them from achieving their goals.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/phuryn/sentiment-analysis/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip sentiment-analysis.zip -d .agents/skills

Documentation by Pawel Huryn

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

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-market-research/skills/sentiment-analysis · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Pawel Huryn or Pawel Huryn.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download sentiment-analysis.zip