asc-aso-audit▌
rudrankriyam/asc-skills · updated Apr 8, 2026
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Run a two-phase ASO audit: offline checks against local metadata files, then keyword gap analysis via Astro MCP.
asc ASO audit
Run a two-phase ASO audit: offline checks against local metadata files, then keyword gap analysis via Astro MCP.
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 exportorasc localizations download, normalize it into the canonical./metadatalayout before running this skill. - For Astro gap analysis: app tracked in Astro MCP (optional — offline checks run without it).
Before You Start
- Read
references/aso_rules.mdto understand the rules each check enforces. - Identify the latest version directory under
metadata/version/(highest semantic version number). Use this for all version-level fields. - The primary locale is
en-USunless 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
nameis missing from the app-info JSON, fetch it viaasc apps info listor 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:
- Read
metadata/app-info/{locale}.jsonforsubtitle(andnameif present) - Read
metadata/version/{latest-version}/{locale}.jsonforkeywords - 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.
- Split keywords by comma, trim whitespace, lowercase
- Report intersection (including fuzzy matches from prefix stripping)
2. Underutilized Fields
Flag fields using less than their recommended minimum:
| Field | Minimum | Limit | Rationale |
|---|---|---|---|
| Keywords | 90 chars | 100 | 90%+ usage maximizes indexing |
| Subtitle | 20 chars | 30 | 65%+ 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:
- Load keywords for all locales
- Compare each non-primary locale against the primary
- 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:
- Load
keywordsanddescriptionfor each locale - For each keyword, check if it appears as a substring in the description (case-insensitive)
- Account for inflected forms: Arabic root matches, verb conjugations (e.g., "memorizar" ≈ "memorices"), and case declensions (e.g., Russian "сура" ≈ "суры")
- Report missing keywords per locale — recommend weaving them naturally into existing sentences
- 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
-
Get current keywords: Call
get_app_keywordswith the app ID to retrieve tracked keywords and their current rankings. -
Ensure multi-store tracking: For each locale with a corresponding App Store territory (e.g.,
ar-SA→ Saudi Arabia,fr-FR→ France,tr→ Turkey), useadd_keywordsto add keyword tracking in that store. Without this,search_rankingsreturns empty for non-US stores. -
Extract competitor keywords: Call
extract_competitors_keywordswith 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. -
Get suggestions: Call
get_keyword_suggestionswith the app ID for additional recommendations based on category analysis. -
Check current rankings: Call
search_rankingsto see where the app currently ranks for tracked keywords in each store. -
Diff against metadata: Compare suggested and competitor keywords against the tokens present in
subtitle,name(if available), andkeywordsfields from the local metadata. -
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_appfor gap analysis" - Store not tracked for a locale → add tracking with
add_keywordsbefore 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 pullto 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" --confirmasc 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).
How to use asc-aso-audit on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add asc-aso-audit
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches asc-aso-audit from GitHub repository rudrankriyam/asc-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate asc-aso-audit. Access the skill through slash commands (e.g., /asc-aso-audit) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★37 reviews- ★★★★★Aditi Johnson· Dec 16, 2024
I recommend asc-aso-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Dec 12, 2024
Useful defaults in asc-aso-audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zara Yang· Dec 8, 2024
asc-aso-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Haddad· Nov 27, 2024
Useful defaults in asc-aso-audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nia Martinez· Nov 7, 2024
Keeps context tight: asc-aso-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Nov 3, 2024
asc-aso-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Naina Kim· Oct 26, 2024
asc-aso-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Oct 22, 2024
Keeps context tight: asc-aso-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zara Lopez· Oct 18, 2024
I recommend asc-aso-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Tariq Ndlovu· Sep 21, 2024
I recommend asc-aso-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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