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Asc Aso Audit

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hanamizuki
asc-aso-audit

Run an offline ASO audit on canonical App Store metadata under `./metadata` and surface keyword gaps using Astro MCP. Use after pulling metadata with `asc metadata pull`.

Overview

Publisherhanamizuki
Repositorysolopreneur
Skill nameasc-aso-audit
Stars
150
Forks
9
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hanamizuki on GitHub. Read the source before you install it.

Installation

Install the Asc Aso Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hanamizuki/solopreneur.git /tmp/solopreneur
mkdir -p .claude/skills
cp -r /tmp/solopreneur/plugins/claude/ios-dev/skills/asc-aso-audit .claude/skills/asc-aso-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asc Aso Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Asc Aso Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Asc Aso Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

asc ASO audit

Run a two-phase ASO audit: offline checks against local metadata files, then keyword gap analysis via Astro MCP. When available, include Apple-generated app tags as a discoverability signal.

Preconditions

  • Metadata pulled locally into canonical files via asc metadata pull --app "APP_ID" --version "1.2.3" --dir "./metadata".
  • If metadata came from asc migrate export or asc localizations download, normalize it into the canonical ./metadata layout before running this skill.
  • For Astro gap analysis: app tracked in Astro MCP (optional — offline checks run without it).
  • For Apple-generated discoverability tags: asc app-tags list --app "APP_ID" --output json works when the API returns tags for the app.

Before You Start

  1. Read references/aso_rules.md to understand the rules each check enforces.
  2. Identify the latest version directory under metadata/version/ (highest semantic version number). Use this for all version-level fields.
  3. The primary locale is en-US unless the user specifies otherwise.

Metadata File Paths

  • App-info fields (subtitle): metadata/app-info/{locale}.json
  • Version fields (keywords, description, whatsNew): metadata/version/{latest-version}/{locale}.json
  • App name: May not be present in exported metadata. If name is missing from the app-info JSON, fetch it via asc apps info list or ask the user. Do not flag it as a missing-field error.

Phase 1: Offline Checks

Run these 5 checks against the local metadata directory. No network calls required.

1. Keyword Waste

Tokenize the subtitle field (and name if available). Flag any token that also appears in the keywords field — it is already indexed and wastes keyword budget.

Severity: ⚠️ Warning
Example:  "quran" appears in subtitle AND keywords — remove from keywords to free 6 characters

How to check:

  1. Read metadata/app-info/{locale}.json for subtitle (and name if present)
  2. Read metadata/version/{latest-version}/{locale}.json for keywords
  3. Tokenize subtitle (+ name):
    • Latin/Cyrillic scripts: split by whitespace, strip leading/trailing punctuation, lowercase
    • Chinese/Japanese/Korean: split by , or iterate characters — each character or character-group is a token. Whitespace tokenization does not work for CJK.
    • Arabic: split by whitespace, then also generate prefix-stripped variants (remove ال prefix) since Apple likely normalizes definite articles. For example, "القرآن" in subtitle should flag both "القرآن" and "قرآن" in keywords.
  4. Split keywords by comma, trim whitespace, lowercase
  5. Report intersection (including fuzzy matches from prefix stripping)

Optional: App Tag Alignment

App tags are Apple-generated labels that can appear in search results and product pages. They are not editable ASO metadata, but they are useful evidence for whether Apple's classification matches the intended positioning.

bash
asc app-tags list --app "APP_ID" --output json
asc app-tags view --app "APP_ID" --id "TAG_ID" --output json

Use tags as context only:

  • If visible tags reinforce the subtitle/keyword strategy, note the alignment.
  • If tags point to an unintended category or use case, recommend metadata/category changes that may improve future classification.
  • Do not promise that changing metadata will immediately change Apple-generated tags.

2. Underutilized Fields

Flag fields using less than their recommended minimum:

FieldMinimumLimitRationale
Keywords90 chars10090%+ usage maximizes indexing
Subtitle20 chars3065%+ usage recommended
Severity: ⚠️ Warning
Example:  keywords is 62/100 characters (62%) — 38 characters of indexing opportunity unused

3. Missing Fields

Flag empty or missing required fields: subtitle, keywords, description, whatsNew.

Note: name may not be in the export — only flag it if the app-info JSON explicitly contains a name key with an empty value.

Severity: ❌ Error
Example:  subtitle is empty for locale en-US

4. Bad Keyword Separators

Check the keywords field for formatting issues:

  • Spaces after commas (quran, recitation)
  • Semicolons instead of commas (quran;recitation)
  • Pipes instead of commas (quran|recitation)
Severity: ❌ Error
Example:  keywords contain spaces after commas — wastes 3 characters

5. Cross-Locale Keyword Gaps

Compare keywords fields across all available locales. Flag locales where keywords are identical to the primary locale (en-US by default) — this usually means they were not localized.

Severity: ⚠️ Warning
Example:  ar keywords identical to en-US — likely not localized for Arabic market

How to check:

  1. Load keywords for all locales
  2. Compare each non-primary locale against the primary
  3. Flag exact matches (case-insensitive)

6. Description Keyword Coverage

Check whether keywords appear naturally in the description field. While Apple does not index descriptions for search, users who see their search terms reflected in the description are more likely to download — this improves conversion rate, which indirectly boosts rankings.

Severity: 💡 Info
Example:  3 of 16 keywords not found in description: namaz, tarteel, adhan

How to check:

  1. Load keywords and description for each locale
  2. For each keyword, check if it appears as a substring in the description (case-insensitive)
  3. Account for inflected forms: Arabic root matches, verb conjugations (e.g., "memorizar" ≈ "memorices"), and case declensions (e.g., Russian "сура" ≈ "суры")
  4. Report missing keywords per locale — recommend weaving them naturally into existing sentences
  5. Do NOT flag: Latin-script keywords in non-Latin descriptions (e.g., "quran" in Cyrillic text) — these target separate search paths

Phase 2: Astro MCP Keyword Gap Analysis

If Astro MCP is available and the app is tracked, run keyword gap analysis. Run this per store/locale, not just for the US store — keyword popularity varies dramatically across markets.

Steps

  1. Get current keywords: Call get_app_keywords with the app ID to retrieve tracked keywords and their current rankings.

  2. Ensure multi-store tracking: For each locale with a corresponding App Store territory (e.g., ar-SA → Saudi Arabia, fr-FR → France, tr → Turkey), use add_keywords to add keyword tracking in that store. Without this, search_rankings returns empty for non-US stores.

  3. Extract competitor keywords: Call extract_competitors_keywords with 3-5 top competitor app IDs to find keyword gaps. This is the highest-value Astro tool — it reveals keywords competitors rank for that you don't. Run this per store when possible.

  4. Get suggestions: Call get_keyword_suggestions with the app ID for additional recommendations based on category analysis.

  5. Check current rankings: Call search_rankings to see where the app currently ranks for tracked keywords in each store.

  6. Diff against metadata: Compare suggested and competitor keywords against the tokens present in subtitle, name (if available), and keywords fields from the local metadata.

  7. Surface gaps: Report all gaps ranked by popularity score (highest first). Include the source (competitor analysis vs. suggestion).

Cross-Field Combo Strategy

When recommending keyword additions, consider how single words combine across indexed fields (title + subtitle + keywords). For example:

  • Adding "namaz" to keywords when "vakti" is already present enables matching the search "namaz vakti" (66 popularity)
  • Adding "holy" to keywords when "Quran" is in the subtitle enables matching "holy quran" (58 popularity)

Flag high-value combos in recommendations.

Skip Conditions

  • Astro MCP not connected → skip with note: "Connect Astro MCP for keyword gap analysis"
  • App not tracked in Astro → skip with note: "Add app to Astro with mcp__astro__add_app for gap analysis"
  • Store not tracked for a locale → add tracking with add_keywords before querying

Output Format

Present results as a single audit report. The report covers only the latest version directory.

### ASO Audit Report

**App:** [name] | **Primary Locale:** [locale]
**Metadata source:** [path including version number]

#### Field Utilization

| Field | Value | Length | Limit | Usage |
|-------|-------|--------|-------|-------|
| Name | ... | X | 30 | X% |
| Subtitle | ... | X | 30 | X% |
| Keywords | ... | X | 100 | X% |
| Promotional Text | ... | X | 170 | X% |
| Description | (first 50 chars)... | X | 4000 | X% |

#### Offline Checks

| # | Check | Severity | Field | Locale | Detail |
|---|-------|----------|-------|--------|--------|
| 1 | Keyword waste | ⚠️ | keywords | en-US | "quran" duplicated in subtitle |

**Summary:** X errors, Y warnings across Z locales

#### Keyword Gap Analysis (Astro MCP)

| Keyword | Popularity | In Metadata? | Suggested Action |
|---------|-----------|--------------|-----------------|
| quran recitation | 72 | ❌ | Add to keywords |

#### Recommendations

1. [Highest priority action — errors first]
2. [Next priority — keyword waste]
3. [Utilization improvements]
4. [Keyword gap opportunities]

Notes

  • Offline checks work without any network access — they read local files only.
  • Astro gap analysis is additive — the audit is useful even without it.
  • Run this skill after asc metadata pull to ensure canonical metadata files are current.
  • For keyword-only follow-up after the audit, prefer the canonical keyword workflow:
    • asc metadata keywords diff --app "APP_ID" --version "1.2.3" --dir "./metadata"
    • asc metadata keywords apply --app "APP_ID" --version "1.2.3" --dir "./metadata" --confirm
    • asc metadata keywords sync --app "APP_ID" --version "1.2.3" --dir "./metadata" --input "./keywords.csv" when importing external keyword research
  • After making changes, re-run the audit to verify fixes.
  • The Field Utilization table includes promotional text for completeness, but no check validates its content (it is not indexed by Apple).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Asc Aso Audit AI skill do?

Run an offline ASO audit on canonical App Store metadata under `./metadata` and surface keyword gaps using Astro MCP. Use after pulling metadata with `asc metadata pull`.

Why use Asc Aso Audit on TypingMind?

Because you install it once and use it with any model. Asc Aso Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Asc Aso Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hanamizuki/solopreneur/tree/main/plugins/claude/ios-dev/skills/asc-aso-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Asc Aso Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Asc Aso Audit?

As many as you like. As long as a model supports skills, you can use Asc Aso Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Asc Aso Audit AI skill free?

Yes. It is published on GitHub by hanamizuki under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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