suede-aso — Claude Code skill: App Store and Google Play listing audit

---
name: suede-aso
description: "Suede AI app-store optimization for keyword fields, titles, subtitles, descriptions, screenshots, ratings context, and competitor listing audits. Use when improving App Store or Google Play visibility or listing conversion from a live app URL and current console evidence, or when authoring keyword, name, subtitle, and promo fields for an app that has not shipped yet. NOT FOR: building or releasing the app (use android-app-factory or site-to-ios-app; native iOS builds are a private Suede AI companion, not in this pack: ios-app-factory), creating paid ad assets (use suede-ad-creative), or install-event instrumentation (use suede-analytics)."
metadata:
  version: 2.0.0
---

# 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

| Tier            | Signals                                                                                                                              | Examples                                    |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------- |
| **Dominant**    | Household name, 1M+ ratings, top-10 in category, near-universal brand recognition. Users search by brand name, not generic keywords. | Instagram, Uber, Spotify, WhatsApp, Netflix |
| **Established** | Well-known in their category, 100K+ ratings, strong organic installs, recognized brand but not universally known.                    | Strava, Notion, Duolingo, Cash App, Calm    |
| **Challenger**  | Building awareness, <100K ratings, needs discovery through keywords and ASO tactics. Most apps fall here.                            | Your app, most indie/startup apps           |

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`: How to use (and not use) third-party benchmarks, directional patterns, and how to build a client baseline from console data

### Dimensions and Weights

| #   | Dimension            | Weight | What It Covers                                                            |
| --- | -------------------- | ------ | ------------------------------------------------------------------------- |
| 1   | Title & Subtitle     | 20%    | Character usage, keyword presence, clarity, brand + keyword balance       |
| 2   | Description          | 15%    | First 3 lines, natural use of core terms (Google), CTA, structure, promotional text |
| 3   | Visual Assets        | 25%    | Screenshot count/quality/messaging, video, icon, feature graphic          |
| 4   | Ratings & Reviews    | 20%    | Average rating, volume, recency, developer responses                      |
| 5   | Metadata & Freshness | 10%    | Category choice, update recency, localization count, data safety          |
| 6   | Conversion Signals   | 10%    | Price positioning, IAP transparency, social proof, download range         |

**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

| Score  | Grade | Meaning                                                   |
| ------ | ----- | --------------------------------------------------------- |
| 85-100 | A     | Well-optimized; focus on A/B testing and iteration        |
| 70-84  | B     | Good foundation; clear opportunities to improve           |
| 50-69  | C     | Significant gaps; prioritized fixes will have high impact |
| 30-49  | D     | Major optimization needed across multiple dimensions      |
| 0-29   | F     | Listing needs a complete overhaul                         |

---

## 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:

### What Apple Indexes vs What Google Indexes

| Field                 | Apple Indexed?   | Google Indexed?        |
| --------------------- | ---------------- | ---------------------- |
| Title                 | Yes              | Yes (strongest signal) |
| Subtitle / Short desc | Yes              | Yes                    |
| Keyword field         | Yes (hidden)     | Does not exist         |
| Long description      | No               | Yes (heavily)          |
| Screenshot captions   | Unconfirmed      | No                     |
| In-app events         | Yes              | N/A (LiveOps instead)  |
| Developer name        | No               | Partial                |
| IAP names             | Yes              | Yes                    |

### Store listings outside the store

App Store and Google Play pages are indexed by web search and often cited by
AI assistants when someone asks "best app for [job]". Two checks on every
audit:

- **Entity match**: developer name, app name, and the one-line description
  match the brand's canonical entity facts (see `suede-ai-seo`). A developer
  name that still shows an old company name splits the entity.
- **Web landing page**: the app has a crawlable page on the brand's site that
  states what it does, links to both store listings, and carries
  `SoftwareApplication` (or `MobileApplication`) JSON-LD matching the store
  facts. Route the page itself to `suede-seo-audit`.

## Pre-Launch Metadata Mode

Use this mode when the app has no live listing yet, so there is no URL to audit.
Search language belongs in the keyword surfaces; product differentiation belongs
in screenshots, the description body, and promo text.

1. Pick one winnable target keyword before the listing is written: users
   plausibly search it, competitors exist but are not all dominant incumbents,
   recent apps show the niche is alive, the app satisfies the searcher's job
   directly, and the term is not a competitor trademark.
2. Write name, subtitle, and keyword field inside the hard App Store limits
   below. Do not repeat a word across the three fields, use no spaces after
   commas in the keyword field, keep one of singular or plural, and front-load
   the highest-value terms.
3. Plan screenshots outcome first: screenshot 1 sells the result, screenshot 2
   sells the differentiator, captions use the searcher's words.
4. Run every claim through a feature-truth check. Never advertise a feature the
   build does not have.
5. Set a 2 to 4 week measurement plan for rankings, impressions, product page
   conversion, and promo text or caption rotation.

Hard limits (confirm against the dated Apple reference file before shipping):
app name 30 characters, subtitle 30, keyword field 100 (comma-separated),
promotional text 170.

Output artifacts:

- `aso/keywords.md` and `aso/aso-doc.md`
- `fastlane/metadata/<locale>/name.txt`, `subtitle.txt`, `keywords.txt`,
  `description.txt`, and `promotional_text.txt`
- a screenshot title plan and the ordered screenshot asset list

---

## Common Issues Checklist

Flag these if found. Items marked _(tier-dependent)_ should be evaluated against
the app's brand maturity tier: they may be deliberate choices for Dominant apps.

Every flag carries an **author action**, so the reader knows what to do with it:

| Action | Meaning |
|---|---|
| **Blocks the listing** | Risks rejection, removal, or ranking suppression. Fix before the next submission. |
| **Required** | Costs measurable installs or conversion. Fix in the next release cycle. |
| **Optional** | Upside, not a defect. Ship if capacity allows. |

**Always flag (all tiers):**

- [ ] Rating below 4.0: **required**
- [ ] Last update > 3 months ago: **required**
- [ ] Google Play description never names the core use-case terms in natural language, or stuffs them as keyword lists: **required**
- [ ] Google Play missing feature graphic: **blocks the listing** (no featured placement without it)
- [ ] Apple keyword field likely has repeated words (inferred from title+subtitle): **required**
- [ ] Category mismatch (app would face less competition in a different category): **required**
- [ ] Fewer than 5 screenshots: **required**

**Flag for Challenger/Established only** _(not mistakes for Dominant apps):_

- [ ] Title wastes characters on brand name only (no keywords): **required** _(Dominant: brand IS the keyword)_
- [ ] Subtitle/short description duplicates title keywords: **required**
- [ ] Description first 3 lines are generic: **required** _(Dominant: may be brand-voice choice)_
- [ ] No preview video: **optional** _(Dominant: may be rational if product is hard to demo)_
- [ ] Screenshots are just UI dumps with no messaging/captions: **required** _(Dominant: lifestyle/brand shots may convert better)_
- [ ] Only 1-2 localizations: **optional** _(score relative to actual market, not absolute count)_
- [ ] No in-app events or promotional content: **optional** _(Dominant utility apps may not need discovery help)_

**Flag for all tiers but note context:**

- [ ] No developer responses to negative reviews: **required** _(note volume: responding at 10M+ reviews is a different challenge than at 1K)_
- [ ] Generic "What's New" text: **optional** _(Apple 2.3.12 makes it **blocks the listing** when the release carries significant changes)_

Prohibited title metadata (emojis, ALL CAPS, "best"/"#1"/"free", CTAs on Google
Play) is always **blocks the listing**: see `references/google-play-specs.md`.

---

## Task-Specific Questions

1. What is the App Store or Google Play URL?
2. Is this your app or a competitor's?
3. What category does the app compete in?
4. Do you have competitor URLs to compare against?
5. Are you focused on search visibility, conversion rate, or both?
6. Do you have access to App Store Connect or Google Play Console data?

---

## Boundaries

- Do not claim keyword rank, conversion lift, review status, or store approval without a current source or console readback.
- Do not edit or submit store metadata, screenshots, builds, prices, or releases without explicit authorization.
- Do not invent competitor performance, customer sentiment, or platform benchmarks; label estimates and their source dates.
- Do not reuse copyrighted competitor assets or generate an alternate Suede S in screenshot recommendations.

## Routing

- Need paid app-install creative -> use `suede-ad-creative`.
- Need install attribution or in-app events -> use `suede-analytics`.
- Need customer language for listing copy -> use `suede-customer-research`.
- Need an iOS or Android product build -> use `site-to-ios-app` or `android-app-factory`; native iOS from scratch is a private Suede AI companion, not in this pack: ios-app-factory.
- Need App Store metadata authored for an app that has not shipped yet -> stay in `suede-aso` and run Pre-Launch Metadata Mode.
- From those skills, route listing audits, metadata strategy, and screenshot sequencing back to `suede-aso`.