insight_skill
11
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
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Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill liangdabiao/market-insight-claude-skill --skill insight- SKILL.md8.7 KB
Overview
This skill helps you rapidly generate user needs insights and prioritize product opportunities for new products, marketing plans, or category entry. It uses a three-stage prompt framework to move from vivid user personas to emotion-driven motivations and then to ranked, actionable product opportunities. You get usable ideas and copy-ready emotional hooks in minutes instead of weeks. The outputs are practical and designed for immediate testing in product design or marketing.
How this skill works
I first identify four concrete user personas based on observable behaviors and value potential, highlighting the core user group with reasons. Next I analyze emotional drivers across pain, itch (identity), and delight to produce vivid, ad-ready expressions. Finally I convert those emotions into a prioritized P0/P1/P2 product opportunity list with implementation notes, cost/difficulty estimates, and quick-win recommendations.
When to use it
- Before building a new product or feature to validate direction quickly
- When preparing go-to-market messaging or campaign hooks
- To evaluate category-entry opportunities and differentiate from incumbents
- When you need concrete user personas rooted in behavior, not demographics
- For rapid prioritization of product concepts that map to real emotions and usage scenarios
- To create MVP experiments and low-cost validation plans based on emotional triggers
Best practices
- Provide a concise product description and current development stage to focus outputs
- Specify analysis scope if you want only personas, only emotions, or only product opportunities
- Pick one core user group for deeper emotion-to-opportunity mapping to keep recommendations actionable
- Treat outputs as hypotheses: run quick experiments before large investments
- Ask for different output formats (executive summary, full report, slides) to match stakeholder needs
- Iterate: compare multiple personas and surface overlapping opportunities for broader plays
Example use cases
- New food concept validating morning-commuter demand and identity signals
- SaaS feature prioritization based on decision-maker pain and ROI anxieties
- Marketing campaign copy that leverages visceral delight moments for higher conversion
- Retail category entry where emotional differentiation beats price competition
- Rapid ideation of 3 ready-to-launch experiments (P0) to test product-market fit
FAQ
A focused analysis takes about 10 minutes to produce structured personas, emotion insights, and prioritized opportunities.
Are these outputs final answers?
No. Treat them as testable hypotheses and use low-cost experiments to validate before scaling.