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 manojbajaj95/claude-gtm-plugin --skill app-store-optimizationgit clone --depth 1 https://github.com/manojbajaj95/claude-gtm-pluginWrote 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/manojbajaj95/claude-gtm-plugin/app-store-optimization)<a href="https://agentmods.dev/skills/manojbajaj95/claude-gtm-plugin/app-store-optimization"><img src="https://agentmods.dev/badge/skills/manojbajaj95/claude-gtm-plugin/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/manojbajaj95/claude-gtm-plugin/app-store-optimization"><img src="https://agentmods.dev/badge/skills/manojbajaj95/claude-gtm-plugin/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 | $0.00031 | $0.03864 |
| Opus 5 | $0.00015 | $0.01932 |
| Sonnet 5 | $0.00006 | $0.00773 |
| Haiku 4.5 | $0.00003 | $0.00386 |
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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Optimization (ASO)
Workspace Context
Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.
Operating Contract
This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.
ASO tools for researching keywords, optimizing metadata, analyzing competitors, and improving app store visibility on Apple App Store and Google Play Store.
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
What ships with it
15 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
- 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.
- 9d ago First seen · 493 lines · 31 tokens per session scan A 59877e0829ae
app-store-optimization is a skill published in the GitHub repository manojbajaj95/claude-gtm-plugin (96 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 3,864 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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