suede-aso

Files (9)

• CARD.md
• SKILL.md
• openai.yaml · agents/
• evals.json · evals/
• apple-specs.md · references/
• benchmarks.md · references/
• google-play-specs.md · references/
• report-template.md · references/
• scoring-criteria.md · references/

SKILL.md (first lines)

# Suede AI App Store Optimization Skills

Read [the worked delivery contract](references/worked-contract.md) for a concrete
input, expected artifact, failure response and validation example.

## Compatibility

Works with live listing URLs or a prelaunch feature brief; web access for current official store field rules, console exports for performance evidence. Store write access is unnecessary for an audit or metadata draft.

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.

---

From JasonColapietro/suede-creator-skills, MIT.

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