Agent skill · Testing & QA

product-appeal-analyzer

Evaluate product desirability, market positioning, and emotional resonance—the complement to friction analysis. Assess whether users will WANT a product (not just use it), identity fit, trust signals, and value proposition clarity. Activate on "will they like it", "market positioning", "appeal analysis", "product desirability", "value proposition", "why would someone choose this", "landing page review", "conversion optimization", "messaging strategy". NOT for UX friction analysis (use ux-friction-analyzer), visual design implementation (use web-design-expert), or A/B test setup (use frontend-d

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill product-appeal-analyzer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Allowed tools: ReadWriteEditWebFetch
Path: skills/analysis/product-appeal-analyzer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Product Appeal Analyzer Evaluate whether users will *want* a product—not just use it. The complement to friction analysis. **Core insight**: Users don't choose the best product—they choose the product that feels most like it was made for them. ## When to Use ✅ **Use for:** - Evaluating landing pages, product pages, app store listings - Positioning a product against alternatives - Crafting messaging, tone, visual identity direction - Assessing emotional resonance with target personas - Pre-launch "will this convert?" analysis ❌ **NOT for:** - UX friction audits (→ use ux-friction-analyzer) - Visual design execution (→ use web-design-expert) - A/B test implementation (→ use frontend-developer) - Market size estimation or financial forecasting - Feature comparison matrices --- ## The Desirability Triangle **All three must be present.** Missing any one kills conversion: ``` IDENTITY FIT "This is for people like me" /\ / \ / \ / ★ \ / DESIRE \ / \ /______________\ PROBLEM TRUST URGENCY SIGNALS "I need this now" "This will actually work" ``` | Missing Element | User Reaction | |-----------------|---------------| | Identity Fit | "Seems useful, but not for me" | | Problem Urgency | "Coo

What's inside
Steps it walks through
  1. When to Use
  2. The Desirability Triangle
  3. Quick Analysis: The 5-Second Test
  4. Analysis Process
  5. Step 1: Identify Target Personas
  6. Step 2: Score the Desirability Triangle
  7. Step 3: Map Objections
  8. Step 4: Generate Recommendations
  9. Common Anti-Patterns
  10. Feature Soup Headline
  11. Screenshot Hero
  12. Trust Ladder Violation
  13. Identity Mismatch
  14. Self-Contained Tools
Ships with 1 file
  • metadata.json
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About this skill
What does the product-appeal-analyzer skill do?

Evaluate product desirability, market positioning, and emotional resonance—the complement to friction analysis. Assess whether users will WANT a product (not just use it), identity fit, trust signals, and value proposition clarity. Activate on "will they like it", "market positioning", "appeal analysis", "product desirability", "value proposition", "why would someone choose this", "landing page review", "conversion optimization", "messaging strategy". NOT for UX friction analysis (use ux-friction-analyzer), visual design implementation (use web-design-expert), or A/B test setup (use frontend-d

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill product-appeal-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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