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customer-research

Extract and score customer insights from transcripts, reviews, and online communities.

Updated Apr 6, 2026
Search Tags:#jobs-to-be-done#research-synthesis#customer-research#persona-development#voice-of-customer#digital-watering-holes
One-click install
npx skills add https://github.com/sicktastic/skill-issue --skill customer-research-sicktastic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/sicktastic/skill-issue/tree/main/marketing/skills/customer-research
Command: npx skills add https://github.com/sicktastic/skill-issue --skill customer-research-sicktastic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Bridges the gap between assumption and reality by extracting, synthesizing, and scoring insights from interviews, reviews, tickets, and online conversations so positioning, product, and messaging are grounded in what customers actually think, feel, say, and struggle with. Check for .agents/product-marketing-context.md or .claude/product-marketing-context.md before asking questions so answered context can be reused.

Core Features & Use Cases

  • Mode 1 – Analyze existing assets: Follow the jobs-to-be-done, pain, trigger, outcome, language, and alternatives framework across interviews, surveys, support tickets, NPS verbatims, and churn notes, then cluster themes, score frequency/intensity, and flag contradictions before offering insights.
  • Mode 2 – Digital watering holes: Mine Reddit, G2/Capterra, forums, SparkToro, LinkedIn, YouTube, and other communities, capture source-context-quote metadata, and assemble high-signal VOC language for copy, messaging, and positioning.
  • Research deliverables: Offer synthesis reports, quote banks, personas (only after 5–10 data points), jobs-to-be-done maps, competitive intel, or gap analyses while applying confidence labels and recency guardrails.

Quick Start

Ask the customer research skill to catalog the available interviews, read the product marketing context if one exists, and surface the jobs to be done along with the highest-confidence pain points.

Frequently Asked Questions about customer-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract jobs-to-be-done and pain points from customer interview transcripts?▼

Customer research extracts jobs-to-be-done, pains, triggers, and desired outcomes from interview transcripts by clustering themes, scoring frequency and intensity, and flagging contradictions before offering insights. It applies confidence labels and recency guardrails to ensure high-fidelity synthesis grounded in actual customer language.

Can I mine Reddit and G2 reviews for voice of customer insights?▼

Yes, you can mine Reddit, G2, Capterra, forums, LinkedIn, and YouTube communities for voice of customer insights. The process captures source-context-quote metadata from these digital watering holes and assembles high-signal VOC language for copy, messaging, and positioning decisions.

What's the best way to synthesize NPS verbatims and support tickets for product positioning?▼

The best way to synthesize NPS verbatims and support tickets is applying a jobs-to-be-done, pain, trigger, outcome, language, and alternatives framework across the data. This clusters themes, scores frequency and intensity, and flags contradictions to ground product positioning in what customers actually struggle with.

How many customer data points do I need before developing personas from research synthesis?▼

You need 5 to 10 data points before persona development from research synthesis. Customer research creates personas only after sufficient data points are collected, ensuring personas are grounded in clustered, scored insights rather than assumptions, with confidence labels applied to all findings.

Does customer research work with existing surveys and churn notes or only new interviews?▼

Customer research works with both existing assets and new investigations. Mode 1 analyzes existing interviews, surveys, support tickets, NPS verbatims, and churn notes, while Mode 2 investigates digital watering holes like Reddit, G2, and forums to capture fresh voice of customer insights.

When should I not use automated customer research synthesis for messaging decisions?▼

You should avoid relying on automated customer research synthesis when you have fewer than 5 to 10 data points for personas, lack recent data due to recency guardrails, or when contradictions in the source material remain unresolved and unflagged by the extraction framework.

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