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 agentmods add skills/raydeveloperf/app-store-connect-release-kit/asc-keywordsnpx skills add raydeveloperF/App-Store-Connect-release-kit --skill asc-keywordsgit clone --depth 1 https://github.com/raydeveloperF/App-Store-Connect-release-kitWrote 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/raydeveloperf/app-store-connect-release-kit/asc-keywords)<a href="https://agentmods.dev/skills/raydeveloperf/app-store-connect-release-kit/asc-keywords"><img src="https://agentmods.dev/badge/skills/raydeveloperf/app-store-connect-release-kit/asc-keywords.svg" alt="Measured on agentmods" 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 | $0.00066 | $0.01101 |
| Opus 5 | $0.00033 | $0.00550 |
| Sonnet 5 | $0.00013 | $0.00220 |
| Haiku 4.5 | $0.00007 | $0.00110 |
Grade A, and why
asc-keywords 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 4d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASC Keywords
Use this skill to produce one or more localized App Store Connect Keywords fields. The deliverable is keyword-only ASO output: no subtitle, no title rewrite, no screenshot copy, no marketing description.
Project Context First
Before keyword research, gather enough context to understand the current app. Do not jump straight from a category label to final keywords.
If a local project or workspace is available, inspect the relevant app files first, such as product docs, README, app entry points, feature screens, localization files, existing App Store metadata, or marketing copy. Keep the pass lightweight and read-only unless the user asks for edits.
Build a short working brief before generating candidates:
- App name, category, and target platforms.
- Core user job and the 3-5 strongest implemented features.
- Target audience, usage scenario, and likely search intent.
- Existing title/subtitle/metadata words that must not be duplicated in
Keywords. - Localization languages and any locale-specific product differences.
- Competitive posture: new indie app, niche app, or already established app.
If the project context is missing or too thin, ask focused questions or request the minimum files/evidence needed. It is acceptable to produce a starter keyword set only after labeling it as context-limited and pending App Store validation.
Required Inputs
The only input that cannot be inferred: target locales (e.g. en-US, zh-Hans).
If target locales are missing and were not provided by the caller, ask once and stop.
Everything else — app category, core user job, seed words — infer from the product brief or project files. Do not ask the user for these.
Candidate Generation
Build candidates from the product brief and project evidence:
- Extract core function words and feature names from the product brief.
- Add locale-native synonyms for those functions and common user search intent words.
- Pull any competitor vocabulary the user or brief already mentions.
- Label the result as "starter set — validate with on-device App Store search before shipping" when no live autocomplete evidence is available.
What ships with it
1 file 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.
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.
- 4d ago First seen · 96 lines · 66 tokens per session scan A 36c885e3a49e
asc-keywords is a skill published in the GitHub repository raydeveloperF/App-Store-Connect-release-kit (2 stars, last pushed 3mo ago), licensed MIT. It adds 66 tokens to every session and 1,101 once invoked, about $0.0003 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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