executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
suede-asoSuede-owned app-store optimization discipline 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 t…
Independent GitHub snapshot · Automated classification · Original documentation governs
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.
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.
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}"
Use WebFetch to retrieve the listing page. Extract every available field:
Apple App Store fields:
Google Play fields:
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.
WebFetch cannot extract screenshot images or caption text. Take a screenshot of the listing page to get visual data:
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.
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 | 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.
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 triggersreferences/google-play-specs.md: Official Google Play limits, screenshot specs, Android Vitals thresholds, policiesreferences/benchmarks.md: Conversion data, rating impact, video lift, screenshot behavior, CPP/event benchmarks| # | Dimension | Weight | What It Covers |
|---|---|---|---|
| 1 | Title & Subtitle | 20% | Character usage, keyword presence, clarity, brand + keyword balance |
| 2 | Description | 15% | First 3 lines, keyword density (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.
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:
| 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 |
If the user provides competitor URLs or asks for comparison:
If no competitors are specified, suggest the user provide 2-3 or offer to search for top apps in their category.
Use the template in references/report-template.md to structure the output.
The report must include:
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:
| 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 | Yes (since 2025) | No |
| In-app events | Yes | N/A (LiveOps instead) |
| Developer name | No | Partial |
| IAP names | Yes | Yes |
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.
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.mdfastlane/metadata/<locale>/name.txt, subtitle.txt, keywords.txt,
description.txt, and promotional_text.txtFlag 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):
Flag for Challenger/Established only (not mistakes for Dominant apps):
Flag for all tiers but note context:
Prohibited title metadata (emojis, ALL CAPS, "best"/"#1"/"free", CTAs on Google
Play) is always blocks the listing: see references/google-play-specs.md.
suede-ad-creative.suede-analytics.suede-customer-research.site-to-ios-app or android-app-factory; native iOS from scratch is a private Suede Labs companion, not in this pack: ios-app-factory.suede-aso and run Pre-Launch Metadata Mode.suede-aso.