Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cass-2003/local-workflow-skill --skill app-store-optimizationgit clone --depth 1 https://github.com/cass-2003/local-workflow-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cass-2003/local-workflow-skill/app-store-optimization)<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/app-store-optimization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/app-store-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.03761 |
| Opus 5.5 | $0.00041 | $0.01504 |
| Sonnet 5.5 | $0.00021 | $0.00752 |
| Haiku 4.5 | $0.00010 | $0.00376 |
Grade A, and why
app-store-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to app-store-optimization — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Optimization (ASO)
Keyword Research Workflow
Discover and evaluate keywords that drive app store visibility.
Workflow: Conduct Keyword Research
- Define target audience and core app functions:
- Primary use case (what problem does the app solve)
- Target user demographics
- Competitive category
- Generate seed keywords from:
- App features and benefits
- User language (not developer terminology)
- App store autocomplete suggestions
- Expand keyword list using:
- Modifiers (free, best, simple)
- Actions (create, track, organize)
- Audiences (for students, for teams, for business)
- Evaluate each keyword:
- Search volume (estimated monthly searches)
- Competition (number and quality of ranking apps)
- Relevance (alignment with app function)
- Score and prioritize keywords:
- Primary: Title and keyword field (iOS)
- Secondary: Subtitle and short description
- Tertiary: Full description only
- Map keywords to metadata locations
- Document keyword strategy for tracking
- Validation: Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field
Keyword Evaluation Criteria
| Factor | Weight | High Score Indicators |
|---|---|---|
| Relevance | 35% | Describes core app function |
| Volume | 25% | 10,000+ monthly searches |
| Competition | 25% | Top 10 apps have <4.5 avg rating |
| Conversion | 15% | Transactional intent ("best X app") |
Keyword Placement Priority
| Location | Search Weight |
|---|---|
| App Title | Highest |
| Subtitle (iOS) | High |
| Keyword Field (iOS) | High |
| Short Description (Android) | High |
| Full Description | Medium |
See: references/keyword-research-guide.md
Metadata Optimization Workflow
Optimize app store listing elements for search ranking and conversion.
Workflow: Optimize App Metadata
- Audit current metadata against platform limits:
- Title character count and keyword presence
- Subtitle/short description usage
- Keyword field efficiency (iOS)
- Description keyword density
- Optimize title following formula:
[Brand Name] - [Primary Keyword] [Secondary Keyword] - Write subtitle (iOS) or short description (Android):
- Focus on primary benefit
- Include secondary keyword
- Use action verbs
- Optimize keyword field (iOS only):
- Remove duplicates from title
- Remove plurals (Apple indexes both forms)
- No spaces after commas
- Prioritize by score
- Rewrite full description:
- Hook paragraph with value proposition
- Feature bullets with keywords
- Social proof section
- Call to action
- Validate character counts for each field
- Calculate keyword density (target 2-3% primary)
- Validation: All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/aso-audit-template.md 4.8 KB
- expected_output.json 5.4 KB
- HOW_TO_USE.md 10 KB
- README.md 15 KB
- references/aso-best-practices.md 12 KB
- references/keyword-research-guide.md 12 KB
- references/platform-requirements.md 9.4 KB
- sample_input.json 723 B
- scripts/ab_test_planner.py 22 KB runs code
- scripts/aso_scorer.py 19 KB runs code
- scripts/competitor_analyzer.py 21 KB runs code
- scripts/keyword_analyzer.py 13 KB runs code
- scripts/launch_checklist.py 28 KB runs code
- scripts/localization_helper.py 22 KB runs code
- scripts/metadata_optimizer.py 20 KB runs code
- scripts/review_analyzer.py 25 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 475 lines · 103 tokens per session scan A 125f2393441d
app-store-optimization is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 3mo ago), licensed MIT. It adds 103 tokens to every session and 3,761 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to app-store-optimization, differing in 16 lines, and is treated as a copy.
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