Suede Aso

Suede ASO Audit

Analyze App Store and Google Play listings with the Suede ASO scoring system. Fetch live listing data, score metadata, visuals, and ratings, then produce a prioritized action plan.

Before Auditing

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Phase 1: Identify Store & Fetch

Detect store type from URL

Apple:  apps.apple.com/{country}/app/{name}/id{digits}
Google: play.google.com/store/apps/details?id={package}

If the user gives an app name instead of a URL, search the web for: site:apps.apple.com "{app name}" or site:play.google.com "{app name}"

Fetch the listing

Use WebFetch to retrieve the listing page. Extract every available field:

Apple App Store fields:

• App name (title): 30 char limit
• Subtitle: 30 char limit
• Description (long): not indexed for search, but matters for conversion
• Promotional text: 170 chars, updatable without new release
• Category (primary + secondary)
• Screenshots (count, order, caption text)
• Preview video (presence, duration)
• Rating (average + count)
• Recent reviews (visible ones)
• Price / in-app purchases
• Developer name
• Last updated date
• Version history notes
• Age rating
• Size
• Languages / localizations listed
• In-app events (if any visible)

Google Play fields:

• App name (title): 30 char limit
• Short description: 80 char limit
• Full description: 4,000 char limit, IS indexed for search
• Category + tags
• Feature graphic (presence)
• Screenshots (count, order)
• Preview video (presence)
• Rating (average + count)
• Recent reviews (visible ones)
• Price / in-app purchases
• Developer name
• Last updated date
• What's new text
• Downloads range
• Content rating
• Data safety section
• Languages listed

If WebFetch returns incomplete data (stores render client-side), note gaps and work with what's available. Ask the user to paste missing fields if critical.

Visual asset assessment

WebFetch cannot extract screenshot images or caption text. Take a screenshot of the listing page to get visual data:

1. Navigate to the listing URL and capture a full-page screenshot
2. Assess the screenshot for: icon quality, screenshot count, caption text, messaging quality, preview video presence, feature graphic (Google Play)
3. If browser tools are unavailable, ask the user to share a screenshot of the listing page

Promotional text (Apple): This 170-char field appears above the description but is often indistinguishable from it in scraped HTML. If you cannot confirm its presence, note this and recommend the user check App Store Connect.

Phase 1.5: Assess Brand Maturity

Before scoring, classify the app into one of three tiers. This determines how you interpret "textbook ASO" deviations: a deliberate brand choice by a household name is not the same as a missed opportunity by an unknown app.

Tier definitions

Classification happens here, before any scoring. The per-dimension adjustments each tier earns live under "Brand Maturity Adjustments" in references/scoring-criteria.md, which Phase 2 loads before scoring: do not restate or re-derive them here.

Key principle: Before docking points, ask: "Is this a mistake or a deliberate choice by a team that has data I don't?" If the app has 1M+ ratings and a dedicated ASO team, assume their choices are data-informed unless clearly wrong.

Phase 2: Score Each Dimension

Score each dimension 0-10 using the criteria in references/scoring-criteria.md. Apply the brand maturity tier adjustments from Phase 1.5.

Reference files for platform specs and benchmarks:

• references/apple-specs.md: Official Apple character limits, screenshot/video specs, CPP/PPO rules, rejection triggers
• references/google-play-specs.md: Official Google Play limits, screenshot specs, Android Vitals thresholds, policies
• references/benchmarks.md: Conversion data, rating impact, video lift, screenshot behavior, CPP/event benchmarks

Dimensions and Weights

Final score = weighted sum of the dimensions that were actually observed.

Unassessable dimensions (read before computing any score)

references/scoring-criteria.md defines score 0 as "cannot assess (data unavailable)". Missing data is the normal case here, not the exception: WebFetch cannot extract screenshots or caption text, and promotional text is indistinguishable in scraped HTML. A 0 for unfetched data is not a bad listing; scoring it as one fabricates a failing grade (an unobserved Visual Assets dimension at 25% weight silently drops an A listing to D).

House rule:

1. A dimension you could not observe is excluded from the weighted denominator, not scored 0. Rescale the remaining weights and state the denominator used ("74/100 across 4 dimensions carrying 75% weight").
2. List every excluded dimension as not assessed, with the reason and what the user would need to supply to close it (a screenshot of the listing page, App Store Connect access).
3. If more than 25% of total weight is unassessed, withhold the letter grade entirely. Report the partial scores and the blocked dimensions instead; do not present a grade the evidence cannot support.

Score interpretation

Phase 3: Competitor Comparison (Optional)

If the user provides competitor URLs or asks for comparison:

1. Fetch 2-3 top competitors in the same category
2. Run the same scoring on each
3. Build a comparison table highlighting where the user's app is weaker/stronger
4. Identify keyword gaps: terms competitors rank for that the user's app doesn't target

If no competitors are specified, suggest the user provide 2-3 or offer to search for top apps in their category.

Phase 4: Generate Report

Use the template in references/report-template.md to structure the output.

The report must include:

1. Score card: table with all 6 dimensions, scores, the weighted denominator actually used, and the grade (withheld per the unassessable-dimension rule when more than 25% of weight went unassessed)
2. Top 3 quick wins: changes that take <1 hour and have highest impact
3. Detailed findings: per-dimension breakdown with specific issues and fixes
4. Keyword suggestions: based on title/description analysis and competitor gaps
5. Visual asset recommendations: specific screenshot/video improvements
6. Priority action plan: ordered list of changes by impact vs effort

Report rules

• Every recommendation must be specific and actionable ("Change subtitle from X to Y" not "Improve subtitle")
• Include character counts for all text recommendations
• Flag platform-specific differences (Apple vs Google) when relevant
• Note what CANNOT be assessed without paid tools (search volume, exact rankings)
• When suggesting keyword changes, explain WHY each keyword matters

Platform-Specific Rules

Character limits, screenshot and video specs, CPP and experiment rules, policy prohibitions, Android Vitals thresholds, editorial curation, and rejection triggers are versioned in the three reference files listed in Phase 2. Read the one for the store being audited before scoring any dimension against a spec: they are the source of truth, and dated platform facts are not repeated here.

The one comparison that drives scoring on every audit stays inline: