Product Appeal Analyzer

What it does

When your agent uses it

• Tasks that involve Positioning and messaging
• Tasks that involve Landing pages
• Tasks that involve UI design

Example prompts

• “will they like it”
• “market positioning”
• “appeal analysis”
• “/product-appeal-analyzer”

Requirements

• Python 3
• Pre-approved tools (allowed-tools): Read, Write, Edit, WebFetch

Workflow steps

4 steps, taken from the step headings in SKILL.md.

1. 1Identify Target Personas
2. 2Score the Desirability Triangle
3. 3Map Objections
4. 4Generate Recommendations

What it can do on your machine

Read from SKILL.md and the folder at commit 6713fc7. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • WebFetch

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Appeal Analyzer loads about 2.4k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 159 tokens; SKILL.md has 886 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~159

When it runs · the whole SKILL.md, loaded when a task matches

~2.4k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~9.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from curiositech/some_claude_skills at commit 6713fc7, republished under its MIT licence (© curiositech). 886 words, ~2,351 tokens.

name

product-appeal-analyzer

description

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, WebFetch

metadata.category

Research & Analysis

metadata.tags

product-strategy, marketing, positioning, value-proposition, conversion, user-research

                    IDENTITY FIT
                    "This is for people like me"
                         /\
                        /  \
                       /    \
                      /  ★   \
                     / DESIRE \
                    /          \
                   /______________\
        PROBLEM               TRUST
        URGENCY               SIGNALS
   "I need this now"     "This will actually work"

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]

© curiositech, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in .claude/skills/product-appeal-analyzer of curiositech/some_claude_skills.

• SKILL.md
• .claude-plugin/plugin.json
• CHANGELOG.md
• references/identity-signals.md
• references/objection-catalog.md
• references/scoring-templates.md
• references/trust-ladder.md
• scripts/appeal_scorer.py

Open the folder on GitHubat commit 6713fc7