product-appeal-analyzer

product-appeal-analyzer is a skill for Claude Code from curiositech/some_claude_skills. It costs 129 tokens per session (2,222 once invoked), scanned A, original, MIT.

A guide to judging whether people will want a product, including its positioning, emotional appeal, identity fit, and value proposition.

In plain words
What is it for?
Use it to assess landing pages, product pages, app-store listings, messaging, visual direction, and pre-launch appeal.
Why use it?
It helps reveal why a product may be usable but still fail to attract or persuade its intended audience.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to assess landing pages, product pages, app-store listings, messaging, visual direction, and pre-launch appeal.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/curiositech/some_claude_skills/product-appeal-analyzer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add curiositech/some_claude_skills --skill product-appeal-analyzer
Clone the repo
git clone --depth 1 https://github.com/curiositech/some_claude_skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for product-appeal-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/curiositech/some_claude_skills/product-appeal-analyzer/github.svg)](https://agentmods.dev/skills/curiositech/some_claude_skills/product-appeal-analyzer)
Your own site
<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/product-appeal-analyzer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/product-appeal-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for product-appeal-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/product-appeal-analyzer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/product-appeal-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 129 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,222 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00129 $0.02222
Opus 5 $0.00064 $0.01111
Sonnet 5 $0.00026 $0.00444
Haiku 4.5 $0.00013 $0.00222

Measured 8d ago against content hash f49886727454, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

product-appeal-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/appeal_scorer.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/product-appeal-analyzer/SKILL.md · 279 lines

How it starts

The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

  1. What is this? (Category recognition)
  2. Who is it for? (Identity signal)
  3. What's the core promise? (Value proposition)
  4. 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

Read the full file on GitHub · 279 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 279 lines · 129 tokens per session scan A f49886727454

Subscribe to this mod's changes

product-appeal-analyzer is a skill published in the GitHub repository curiositech/some_claude_skills (219 stars, last pushed 5d ago), licensed MIT. It adds 129 tokens to every session and 2,222 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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