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 AppKittie/aso-mcp-skills --skill app-marketing-contextgit clone --depth 1 https://github.com/AppKittie/aso-mcp-skillsWrote 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/appkittie/aso-mcp-skills/app-marketing-context)<a href="https://agentmods.dev/skills/appkittie/aso-mcp-skills/app-marketing-context"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/app-marketing-context/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/appkittie/aso-mcp-skills/app-marketing-context"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/app-marketing-context.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 | $0.00074 | $0.00859 |
| Opus 5 | $0.00037 | $0.00430 |
| Sonnet 5 | $0.00015 | $0.00172 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
app-marketing-context 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 12d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Marketing Context
You are setting up a shared context document that all AppKittie skills will reference. This ensures consistent, personalized advice across keyword research, competitor analysis, metadata optimization, and more.
When to Use
Run this skill:
- First time using AppKittie skills for a specific app
- When the app's strategy changes (new target market, repositioning)
- When competitors shift significantly
Information to Gather
Ask the user for each section. If they have an App Store ID, use get_app_detail to pre-fill what you can.
1. App Identity
- App name (brand)
- App Store ID (numeric) or slug
- One-sentence description — what does the app do?
- Target platform — iOS only, or also Android?
2. Target Audience
- Primary audience — who is the main user?
- Secondary audience — any secondary segments?
- User problems — what pain points does the app solve?
- User language — how do users describe their problem? (important for keywords)
3. Competitive Landscape
- Direct competitors — apps that do the same thing (list 3–5)
- Indirect competitors — apps that solve the same problem differently
- Key differentiators — what makes this app unique?
4. Current Performance
Use get_app_detail if available:
- Downloads/month (estimated)
- Revenue/month (estimated)
- Rating and review count
- Current keywords (if known)
- Running ads? Use
search_appsfor Meta/Apple presence signals andsearch_ads(appSlug)for creative records
5. Goals
- Primary goal — downloads, revenue, ratings, brand awareness?
- Target metrics — specific numbers they're aiming for
- Timeline — when do they want to achieve this?
- Budget — any ad budget or is it organic only?
Output Format
Generate an app-marketing-context.md file:
# App Marketing Context
## App
- **Name:** [name]
- **App Store ID:** [id]
- **Description:** [one-sentence]
- **Category:** [primary genre]
- **Price:** [free/paid/subscription]
## Audience
- **Primary:** [description]
- **Secondary:** [description]
- **Pain points:** [list]
- **Language:** [how users describe the problem]
## Competitors
| App | Strengths | Weaknesses |
|-----|-----------|-----------|
| [comp1] | [strengths] | [weaknesses] |
## Current Performance
- **Downloads/mo:** [est.]
- **Revenue/mo:** [est.]
- **Rating:** [★] ([count] reviews)
- **Known keywords:** [list]
## Goals
- **Primary:** [goal]
- **Target:** [metrics]
- **Timeline:** [timeframe]
- **Budget:** [amount or "organic only"]
## Notes
[Any additional context]
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.
- 12d ago First seen · 110 lines · 74 tokens per session scan A 6bf7b43bd0d8
app-marketing-context is a skill published in the GitHub repository AppKittie/aso-mcp-skills (6 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 859 once invoked, about $0.0004 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 2026-08-31.
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