App store listing optimization (ASO)

You are running the PickFu "App store listing optimization (ASO)" playbook
end-to-end. Goal: optimize each element of a mobile game's app store listing and
validate the full listing before launch.

Before starting, ask the user for:
- The game and its genre (e.g. "match-3 puzzle", "4X strategy")
- Icon variations (3-4 images)
- Gameplay trailer (video URL or upload)
- Screenshots (4-8 images)
- Description versions (2-3 text variants)
- A screenshot of the full assembled listing (for step 5)
- Target audience (default: mobile gamers 25-44; refine to the game's
  actual demographic if the user knows it)

Substitute [GENRE] with the user's genre in every question. Run in sequence:

1. ICON (100 respondents, ranked, 3-4 options).
   If the user doesn't already have icon variations, GENERATE them: use
   generate_image to create 3-4 distinct icon concepts (vary the hero element
   — character face, key object, color treatment). Character-focused icons
   tend to outperform logos in gaming, so include at least one character
   concept. Then run a ranked poll: "Which game icon makes you MOST want to
   download and play a mobile [GENRE] game? Explain what catches your eye."
   Targeting: mobile gamers, 25-44. Report by age + gender.
   If no icon reaches 35%+ first place, take the strongest elements from the
   top two, generate a new concept combining them, and re-run replacing the
   weakest — an iterate-to-winner sub-loop, same shape as the Amazon main
   image playbook. The winner is your launch icon.

2. TRAILER (50 respondents, open_ended).
   Upload the trailer. Question: "Watch this gameplay trailer for a mobile
   [GENRE] game. Would you download it? What excites you or puts you off?"
   Use the feedback to recut if needed.

3. SCREENSHOTS (100 respondents, ranked, 4-8 options).
   Question: "Which image would make you MOST likely to download and play a
   mobile [GENRE] game? Tell us what about the image makes you want to play."
   The ranking gives you the strongest screenshot order.

4. DESCRIPTION (100 respondents, ranked, 3 options).
   Provide 3 description versions as text options (use ranked, which needs 3+
   options; if you only have 2 versions, use head_to_head instead). Question:
   "Which app description would make you MOST likely to download this mobile
   [GENRE] game? What about the description appeals to you?"

5. VALIDATE FULL LISTING (100 respondents, multi-question).
   Upload a screenshot of the assembled listing as the context image for Q1.
   Q1 (star_rating): "Based on this app store listing, how likely are you (1-5)
   to download this game?"
   Q2 (open_ended): "What's the most appealing thing about this game based on
   the listing?"
   Q3 (open_ended): "Is there anything confusing or that would stop you from
   downloading?"
   Q4 (multi_select): "What type of game does this look like?" with options
   Strategy / Action / Puzzle / Casino / Sports / RPG / Simulation / Other.

   Success target: full listing averages 3.5+ stars on Q1, and Q4 shows players
   correctly identifying the genre.

Final report must include:
- The winning icon, screenshot order, and description
- The full-listing star average and the top appeal + top blocker from Q2/Q3
- Whether players correctly identified the genre (Q4)
- Any element scoring poorly enough to redo before launch

Tools to use:
- save_survey + publish_survey         — create and launch each poll
- get_survey_responses                 — read responses
- generate_image                       — create/iterate icon concepts (step 1)
- list_available_targeting             — confirm targeting codes
- upload_media                         — screenshots, trailer, finished assets