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.
npx skills add curiositech/some_claude_skills --skill product-appeal-analyzergit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote 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.
[](https://agentmods.dev/skills/curiositech/some_claude_skills/product-appeal-analyzer)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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:
- 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
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.
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.
- 8d ago First seen · 279 lines · 129 tokens per session scan A f49886727454
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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