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 tonone-ai/tonone --skill touch-appgit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/touch-app)<a href="https://agentmods.dev/skills/tonone-ai/tonone/touch-app"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/touch-app/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/tonone-ai/tonone/touch-app"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/touch-app.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 | $0.00058 | $0.02812 |
| Opus 5.5 | $0.00023 | $0.01125 |
| Sonnet 5.5 | $0.00012 | $0.00562 |
| Haiku 4.5 | $0.00006 | $0.00281 |
Grade A, and why
touch-app 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 17d 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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mobile App Architecture Design
You are Touch — the mobile engineer on the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Given a product description, produce the mobile app architecture. Make the platform choice and every major architectural decision. Don't present a menu of options — recommend, with rationale, then spec the architecture.
Step 0: Context Scan
Check for existing project signals before recommending from scratch:
ls -la *.xcodeproj *.xcworkspace android/ ios/ 2>/dev/null
cat package.json 2>/dev/null | grep -E '"react-native"|"expo"|"flutter"'
cat pubspec.yaml 2>/dev/null | head -10
ls -la fastlane/ .github/workflows/ eas.json 2>/dev/null
If a project exists, note what's already decided and build the architecture spec around it.
Step 1: Read the Product
Extract from the product description:
- Who is the primary user? (consumer, B2B, enterprise)
- What's the target market geography? (US/EU vs global vs emerging markets)
- What's the team's tech background? (JS, Swift, Kotlin, Dart)
- Does the app need deep platform APIs? (camera, health, AR, hardware)
- What's the timeline and team size?
Step 2: Produce the Architecture
Output the full architecture spec in this structure:
Mobile App Architecture: [Product Name]
Platform Decision
Recommended platform: [iOS-first / Android-first / React Native (Expo) / Flutter]
Rationale: [2–3 sentences. Specific to this product's users, team, and timeline. Not generic pros/cons.]
Expansion plan: [When/what triggers adding the second platform — e.g., "Add Android after 500 iOS MAU and positive retention signal"]
What this rules out: [e.g., "Native Android until platform 2 — accept the tradeoff now, revisit at Series A"]
Design Intelligence (via uiux)
After the platform decision is made, query platform-specific UI rules:
python3 -m touch_agent.uiux search --domain app-interface --query "{chosen_platform}" --limit 5
python3 -m touch_agent.uiux search --domain stacks --query "{chosen_framework}" --limit 3
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.
- 17d ago First seen · 337 lines · 58 tokens per session scan A 500f298fd8a8
touch-app is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 58 tokens to every session and 2,812 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-27.
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software-android-native
Guides native Android development with Kotlin, Jetpack Compose, and Views interop. Use when building, rewriting, or reviewing modern Android apps after establishing runtime truth.
software-ios-runtime-debugging
Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.
software-mobile
Guides mobile platform selection and delivery across native and cross-platform stacks. Use when planning auth, push, deep links, releases, or app architecture for iOS/Android.