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-developer).
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-developer).
allowed-tools
Read,Write,Edit,Bash,WebFetch
metadata
{"category":"Content & Marketing","tags":["product-analysis","appeal","market-fit","user-research","positioning"],"provenance":{"kind":"first-party","owners":["port-daddy"]},"pairs-with":[{"skill":"port-daddy-users","reason":"Supplies the 24 concrete named personas (with segment, goals, friction tolerance, dealbreakers) that satisfy this skill's target-personas input when the product being evaluated is Port Daddy itself."},{"skill":"agentic-coding-product-research","reason":"Supplies persona/audience research (user stories, unmet needs) this skill's per-persona desirability scoring depends on when the product is an agentic coding tool."},{"skill":"agentic-coding-ux-designer","reason":"Turns this skill's appeal recommendations into concrete flows (prompt-to-diff, onboarding, checkpoint rollback) for agentic coding product surfaces."},{"skill":"ux-friction-analyzer","reason":"The complement — appeal answers \"do they want it\", friction answers \"can they use it\"; run both on the same surface."},{"skill":"web-design-expert","reason":"Executes the visual identity and layout the desirability triangle's identity-fit vertex calls for."}],"io-contract":{"kind":"deliverable","consumes":["[Truncated]","[Truncated]","[Truncated]"],"produces":["[Truncated]","[Truncated]"]}}
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
"Cool, maybe someday"
Trust Signals
"Looks sketchy / too good to be true"
Decision tree: When analyzing, score each vertex 1-10. If any is <5, that's your priority fix.
Quick Analysis: The 5-Second Test
Within 5 seconds of landing, a visitor should know:
What is this? (Category recognition)
Who is it for? (Identity signal)
What's the core promise? (Value proposition)
What do I do next? (Clear CTA)
How to run it:
Show landing page to someone unfamiliar for exactly 5 seconds
Hide it, then ask: "What was that? Who's it for? What would you do there?"
Record verbatim—don't coach or clarify
Scoring:
Result
Score
Action
All 4 clear in <3 sec
9-10
Ship it
All 4 clear in 3-5 sec
7-8
Minor polish
3 of 4 clear
5-6
Fix the gap
2 or fewer clear
2-4
Significant rework
Confusing/unclear
0-1
Start over
Analysis Process
Step 1: Identify Target Personas
For each persona, document:
Who: One-sentence description
Problem: What's broken + how it feels
Current workaround: What they do today (and why it sucks)
Identity: How they see themselves, who they want to become
Step 2: Score the Desirability Triangle
For each persona:
PERSONA: [Name]
IDENTITY FIT [/10]
Visual identity match [/10] "Does this look like my kind of tool?"
Language resonance [/10] "Do they speak my language?"
Implied user match [/10] "Are people like me shown?"
PROBLEM URGENCY [/10]
Pain point acknowledged [/10] "They understand my problem"
Emotional resonance [/10] "They get how frustrating it is"
Solution clarity [/10] "I see how this fixes it"
TRUST SIGNALS [/10]
Professional execution [/10] "This looks legitimate"
Social proof [/10] "Others like me use it"
Risk reduction [/10] "What if it doesn't work?"
OVERALL APPEAL SCORE: [/90]
Once the Desirability Triangle scores, 5-second test result, and anti-pattern
flags from the Analysis Process are captured as a structured JSON spec
(matching schemas/appeal-spec.schema.json), run:
node scripts/appeal_audit.mjs --input <spec>.json
This is a deterministic complement to appeal_scorer.py, not a duplicate:
appeal_scorer.py interactively drafts the analysis from a live URL;
appeal_audit.mjs re-checks an already-scored, structured spec against this
skill's own gates (any triangle vertex <5, a failed 5-second test,
trust-ladder violation, identity mismatch, feature-soup headline, screenshot
hero) and returns { pass, findings, recommendations } with no text/keyword
matching involved — every flag it checks is a number or boolean the analyst
already decided. See examples/sample-input.json for a passing spec.
Reference Files (See for deep dives)
File
When to Use
references/scoring-templates.md
Full scoring matrices and templates
references/trust-ladder.md
Deep dive on trust building stages
references/identity-signals.md
Visual/verbal identity signal catalog
references/objection-catalog.md
Common objections by product type
schemas/appeal-spec.schema.json
Validate a structured appeal-audit input programmatically
examples/sample-input.json
A complete spec that appeal_audit.mjs scores pass: true
examples/expected-output.md
Shape of a finished appeal analysis + scorecard
templates/output-template.md
Reusable appeal-analysis template to fill in
agents/openai.yaml
Subagent descriptor for delegated appeal analysis
Output Format
When running this skill, produce:
Executive Summary - 3 bullet key findings
Desirability Triangle Scores - Per persona
5-Second Test Assessment - What's clear, what's not
Top 3 Objections - And how to address them
Priority Recommendations - Immediate / Medium / Long-term
Integration with ux-friction-analyzer
Appeal + Friction = Complete picture
This Skill Answers
ux-friction-analyzer Answers
"Do they want it?"
"Can they use it?"
Will they choose this over alternatives?
Can they complete the task?
Does it feel made for them?
Does the flow make sense?
Is the promise compelling?
Is the experience smooth?
Run both: High appeal + high friction = frustrated users. Low friction + low appeal = abandoned product.
Philosophy: A product with low friction but low appeal gets abandoned. A product with high appeal but high friction gets frustrated users. You need both.
Skill Bundle Index
Every file in this skill, and when to open it. Auto-generated; run scripts/index_references.py --fix.
root
CHANGELOG.md — Changelog — All notable changes to this skill will be documented here.
README.md — Product Appeal Analyzer — Evaluate whether users will want a product — not just whether they can use it.
examples/expected-output.md — Example Output: Product Appeal Analysis — Scenario: Reviewing a developer-tools landing page ahead of launch, for two personas — a solo indie hacker and a staff engineer evaluati
