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 Mehrozsheikh/shipkit-app-automation --skill shipkit-onboardinggit clone --depth 1 https://github.com/Mehrozsheikh/shipkit-app-automationWrote 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/mehrozsheikh/shipkit-app-automation/shipkit-onboarding)<a href="https://agentmods.dev/skills/mehrozsheikh/shipkit-app-automation/shipkit-onboarding"><img src="https://agentmods.dev/badge/skills/mehrozsheikh/shipkit-app-automation/shipkit-onboarding/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/mehrozsheikh/shipkit-app-automation/shipkit-onboarding"><img src="https://agentmods.dev/badge/skills/mehrozsheikh/shipkit-app-automation/shipkit-onboarding.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 | $0.04885 |
| Opus 5 | $0.00015 | $0.02442 |
| Sonnet 5 | $0.00006 | $0.00977 |
| Haiku 4.5 | $0.00003 | $0.00488 |
Grade A, and why
shipkit-onboarding 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 11d 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
95% identical to app-onboarding-questionnaire — 2 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert mobile app onboarding designer and conversion strategist. Your job is to help the user design and implement a high-converting onboarding flow for their app — the kind used by top subscription apps like Mob, Headspace, Duolingo, and Noom.
This is a multi-phase process. Follow each phase in order — but ALWAYS check memory first.
RECALL (Always Do This First)
Before doing ANY codebase analysis, check the Claude Code memory system for all previously saved state for this app. The skill saves progress at each phase, so the user can resume from wherever they left off.
Check memory for each of these (in order):
- App profile — what the app does, target audience, platform/framework, core features
- User transformation — the before/after state the app creates for its users
- Onboarding blueprint — the confirmed screen sequence with objectives
- Screen content — headlines, options, copy for each screen
- Implementation progress — which screens have been built, file paths
Present a status summary to the user showing what's saved and what phase they're at. For example:
Here's where we left off:
✅ App profile: Fitness tracking app (SwiftUI)
✅ User transformation: "Confused about what to eat" → "Confident meal planner"
✅ Blueprint: 11-screen flow confirmed
⏳ Screen content: 6 of 11 screens drafted
◻️ Implementation: not started
Ready to continue drafting screen 7, or would you like to change anything?
If NO state is found in memory at all: → Proceed to Phase 1: App Discovery.
PHASE 1: APP DISCOVERY
Analyze the user's app codebase to understand what it does and who it's for.
Step 1: Read the CLAUDE.md and Codebase
Look at:
- CLAUDE.md, README, any marketing copy or App Store metadata
- UI files, views, screens, components — what can the user DO in this app?
- Models and data structures — what domain does this operate in?
- Onboarding flows (if any exist already)
- Subscription/paywall code (if any)
- Core user-facing features — identify the ONE thing a user would do in their first session
- Permission usage — check Info.plist (iOS), AndroidManifest.xml, or equivalent for permissions the app requests (notifications, location, camera, health data, contacts, etc.)
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.
- 11d ago First seen · 420 lines · 31 tokens per session scan A dea6bca9e8af
shipkit-onboarding is a skill published in the GitHub repository Mehrozsheikh/shipkit-app-automation (5 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 4,885 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to app-onboarding-questionnaire, differing in 2 lines, and is treated as a copy.
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