Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps.

MITAuto-check passedAgent Workflows

Install App Onboarding Questionnaire

skills CLI
$ npx skills add adamlyttleapps/claude-skill-app-onboarding-questionnaire --skill app-onboarding-questionnaire -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install adamlyttleapps/claude-skill-app-onboarding-questionnaire app-onboarding-questionnaire --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
app-onboarding-questionnaire
GitHub stars
1.2k
Used in
1 other repo
Token cost
~5.5k tokens
SKILL.md length
3,063 words
Files
4
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps.

  • Works in 5 steps: APP DISCOVERY → USER TRANSFORMATION → ONBOARDING BLUEPRINT → …
  • Agent Workflows work in your project
  • SKILL.md covers RECALL (Always Do This First), PHASE 1: APP DISCOVERY, PHASE 2: USER TRANSFORMATION and PHASE 3: ONBOARDING BLUEPRINT, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

App Onboarding Questionnaire is an agent skill from adamlyttleapps/claude-skill-app-onboarding-questionnaire. Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps.

Its SKILL.md is about 5.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `CLAUDE.md` and `README.md`).

It sits in Agent Workflows. The repository describes itself as: A Claude Code skill that designs and builds high-converting questionnaire-style app onboarding flows — modelled on proven conversion patterns from top subscription apps like Mob…. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/app-onboarding-questionnaire”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. APP DISCOVERY
  2. USER TRANSFORMATION
  3. ONBOARDING BLUEPRINT
  4. SCREEN CONTENT
  5. IMPLEMENTATION

What it can do on your machine

Read from SKILL.md and the folder at commit 5bc4786. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

App Onboarding Questionnaire loads about 5.5k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 3,063 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from adamlyttleapps/claude-skill-app-onboarding-questionnaire at commit 5bc4786, republished under its MIT licence (© adamlyttleapps). 3,063 words, ~5,479 tokens.

Download SKILL.mdSave it as .claude/skills/app-onboarding-questionnaire/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
app-onboarding-questionnaire
description
Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps.
user-invocable
true

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):

  1. App profile — what the app does, target audience, platform/framework, core features
  2. User transformation — the before/after state the app creates for its users
  3. Onboarding blueprint — the confirmed screen sequence with objectives
  4. Screen content — headlines, options, copy for each screen
  5. 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.)

Build a mental model of:

  • What the app does (core functionality in one sentence)
  • Who it's for (target audience)
  • The core loop (the repeated action that makes the app valuable)
  • The "aha moment" (when a new user first experiences value)
  • Existing paywall/subscription (present or not, type, pricing)
  • Permissions required (notifications, location, camera, health, etc. — detected from the codebase)
Step 2: Ask the User Clarifying Questions

Present what you've learned and ask targeted questions. Only ask what the code doesn't already answer:

  • "Based on the code, this is [X]. Is that right?"
  • "Who is your target user? What's their skill level?"
  • "What's the #1 reason someone downloads this app?"
  • "What problem does this solve that other apps don't?"
  • "Do you want to include sign-in/account creation in onboarding? (optional)"
  • "Do you have a paywall? If yes, what's the pricing? If no, we'll add a placeholder."

Save to memory: app profile (what it does, who it's for, platform, core features, paywall status, sign-in preference).


PHASE 2: USER TRANSFORMATION

This is the most important conceptual step. Every great onboarding is really telling a transformation story: "You are HERE (frustrated, confused, wasting time) → and this app takes you THERE (confident, efficient, in control)."

Step 1: Define the Before & After

Work with the user to articulate:

BEFORE (without the app):

  • What frustrations does the user have?
  • What are they doing instead? (the "bad alternatives")
  • What pain points drive them to search for this app?
  • What negative emotions are they feeling?

AFTER (with the app):

  • What can they now do that they couldn't before?
  • What feelings replace the frustrations?
  • What tangible outcome do they get?
  • What would they tell a friend about why they use this app?
Step 2: Extract the Core Benefit Statements

From the transformation, extract 3-5 benefit statements. These must:

  1. Be specific and measurable where possible — "Save 2 hours a week on meal planning" not "Save time"
  2. Address a real pain point from the BEFORE state
  3. Lead with what the USER gets, not what the app does
  4. Be believable — stretch goals are fine, fantasy is not

Present to the user for confirmation:

Here's the transformation story I'd recommend:

BEFORE: [1-2 sentences describing the frustration]
AFTER: [1-2 sentences describing the outcome]

Core benefits:
1. [Benefit] — addresses [pain point]
2. [Benefit] — addresses [pain point]
3. [Benefit] — addresses [pain point]

Save to memory: confirmed user transformation + benefit statements.


PHASE 3: ONBOARDING BLUEPRINT

Now design the screen-by-screen flow. The blueprint follows a proven psychological sequence. Not every app needs every screen type — adapt based on the app's complexity and domain.

The Onboarding Framework

The flow uses 11 screen archetypes. You MUST include screens marked [REQUIRED]. Others are [RECOMMENDED] or [OPTIONAL] based on fit.

Screen 1: WELCOME [REQUIRED]

Objective: Hook — show the end state, create desire.

  • Bold headline stating the transformation outcome (not the app name)
  • Show a preview/mockup of the app in use (reference an actual app screen from the codebase)
  • "Get Started" primary CTA
  • "Log in" text link (only if user wants sign-in)
  • Progress bar at top (shows throughout entire flow)

Pattern: "Welcome to your new [transformation outcome]" + device preview showing the app's best screen.

Screen 2: GOAL QUESTION [REQUIRED]

Objective: Get the user to self-identify their primary goal. This creates psychological investment — they've now told the app what they want, which makes them feel the app owes them a solution.

  • "What are you trying to achieve?" (or domain-appropriate question)
  • Single-select list of 5-7 goals relevant to the app's domain
  • Each option has an emoji icon + short label
  • Selection highlights with accent colour, reveals "Continue" button

Key principle: The options must be specific enough that users think "yes, that's exactly me" — not generic. Derive these from the app's actual feature set and target audience segments.

Screen 3: PAIN POINTS [REQUIRED]

Objective: Surface the user's frustrations. This does two things: (1) makes them feel understood, (2) gives you ammunition for the solution screen later.

  • "What prevents you from [achieving their goal]?" (reference their Screen 2 answer if possible)
  • Multi-select list of 5-7 pain points
  • Checkbox-style selection (multiple allowed)
  • "Continue" button always visible

Key principle: Pain points should be emotionally resonant and specific. "Lack of time" works. "Suboptimal workflow" does not. Use the language real users would use.

Objective: Reduce risk perception. "Others like me have succeeded with this."

  • "We've helped thousands of others like you" (adapt number if the app has real stats)
  • 2-3 testimonial cards
  • Each testimonial has: name/initials, persona tag (e.g. "Busy professional", "Beginner"), review text
  • Tags should match the audience segments from Screen 2

Key principle: If the app doesn't have real testimonials yet, suggest the user writes aspirational ones based on the transformation they want users to experience. Mark these as placeholder content to be replaced with real reviews later.

Objective: Deepen emotional engagement through interactive self-identification.

  • "Which statements do you relate to?"
  • Large card with a pain/frustration statement in quotes
  • Swipe right (✓) to agree, swipe left (✗) to dismiss
  • 3-5 cards, each stating a common frustration in the user's domain
  • Feels playful and interactive, not like a survey

Key principle: These statements should be things the user will nod along to. They're designed to make the user think "this app really gets me." Use first-person language: "I spend too much time on..." not "Users often struggle with..."

Screen 6: PERSONALISED SOLUTION [REQUIRED]

Objective: Mirror back their pain points and show how the app specifically solves each one. This is the "bridge" moment — "you told us your problems, here's exactly how we fix them."

  • "Welcome to a smarter way to [domain activity]"
  • List of 3-4 items, each showing:
    • Their stated pain point (greyed/small text)
    • The app's solution with a compelling stat or promise (bold text)
  • Each item has a relevant icon/illustration

Key principle: The stats should be specific and credible. "Users save an average of 25% on X" is better than "Save money." If the app is new and has no stats, use industry benchmarks or logical projections.

Screen 7: COMPARISON TABLE [OPTIONAL]

Objective: Make the with/without contrast visceral and obvious.

  • Bold stat headline: "[X]% of people struggle with [problem]"
  • Comparison table: App Name vs "Without"
  • 3-4 rows comparing outcomes (green ✓ vs red ✗)
  • Simple, scannable, no ambiguity about which side wins

Objective: Functional personalisation — collect preferences that make the upcoming demo relevant to them. Also deepens investment (they're customising "their" experience).

  • "What do you like?" or domain-appropriate preference question
  • Grid of options with images/icons (2-column grid works well)
  • Multi-select with visual highlight on selection
  • Can be 1-2 screens depending on how many preference dimensions matter

Key principle: Only ask for preferences that will visibly affect the demo in the next phase. Don't ask questions that go nowhere — users notice.

Screen 9: PERMISSION PRIMING [AUTO-DETECTED]

Objective: Pre-sell the user on granting permissions BEFORE the system dialog appears. A cold system prompt ("App wants to send you notifications") converts at ~40%. A primed request with context converts at 70-80%+.

How to determine which permissions to prime:

  1. Auto-detect from the codebase — scan Info.plist (iOS), AndroidManifest.xml (Android), or framework-equivalent for declared permissions: notifications, location, camera, microphone, health data, contacts, photos, motion, tracking (ATT), etc.
  2. If no permissions are detected, skip this screen entirely.
  3. If multiple permissions are needed, show one priming screen per permission. Order them by how essential they are to the core experience — most important first.

Screen layout for each permission:

  • Headline explaining the VALUE of the permission, not the permission itself
    • Notifications: "Never miss [the thing they care about]" — not "Enable notifications"
    • Location: "Find [things] near you" — not "Allow location access"
    • Camera: "Scan/capture [the thing]" — not "Allow camera access"
    • Health: "Track your [metric] automatically" — not "Allow health access"
  • 2-3 bullet points showing what they'll get by granting the permission
  • An illustration or icon representing the benefit (not a phone settings screenshot)
  • "Enable" primary CTA → triggers the actual system permission dialog
  • "Not now" secondary text link → skips without asking (never punish this choice)

Key principles:

  • NEVER trigger the system permission dialog without priming first. You only get one shot — if the user denies, you're stuck with Settings deep-linking forever.
  • Frame every permission around the USER's benefit, never the app's need.
  • If a permission isn't essential to the core experience, consider deferring it to an in-context moment later in the app (e.g., ask for camera permission when they first tap "Scan", not during onboarding).
  • Only prime permissions that are genuinely needed. Asking for permissions the app doesn't use erodes trust.
  • For notifications specifically: prime AFTER the user has experienced the app demo (Screen 11) if possible — they'll better understand what they'd be notified about. However, if the notification permission is needed for the demo itself, prime it here before the processing moment.

Platform considerations:

  • iOS: Notification permission is one-shot via UNUserNotificationCenter. ATT (App Tracking Transparency) must be prompted separately and has specific Apple guidelines.
  • Android 13+: Notification permission requires runtime prompt (POST_NOTIFICATIONS). Below 13, notifications are granted by default.
  • React Native / Flutter: Use the appropriate permission library (react-native-permissions, permission_handler, etc.)
Screen 10: PROCESSING MOMENT [REQUIRED]

Objective: Build anticipation. Signal that personalisation is happening.

  • Animated loading state (simple animation — spinning icon, pulsing graphic)
  • "[Preparing/Building/Creating] [output] just for you..."
  • Brief pause (1-3 seconds) — even if nothing is actually loading
  • Auto-advances to next screen

Key principle: This screen exists purely for psychological effect. It makes the next screen feel earned and personalised, even if the "processing" is instant.

Show full SKILL.md (1,234 more words)Show less
Screen 11: APP DEMO [REQUIRED — THIS IS THE HARDEST AND MOST IMPORTANT SCREEN]

Objective: Let the user actually USE the core app mechanic inside the onboarding. This is not a tour or a screenshot — it's a functional mini-version of the app's primary interaction.

How to identify the demo:

  1. Look at the app's core loop — what's the ONE thing users do repeatedly?
  2. Reduce it to its simplest form — pick 3 items, make 1 choice, complete 1 action
  3. The demo must produce a TANGIBLE OUTPUT the user can see/share

Examples by app type:

  • Recipe app → Swipe to pick 3 recipes → generates a shopping list
  • Fitness app → Choose 3 exercises → generates a workout plan
  • Finance app → Categorise 5 transactions → shows spending summary
  • Learning app → Answer 3 questions → shows skill level assessment
  • Task app → Add 3 tasks → shows organised daily plan

Implementation approach:

  • Build this as a real, functional screen using the app's actual data models and UI components
  • Use Tinder-style swipe cards or simple tap-to-select mechanics — keep interaction dead simple
  • Show progress: "Pick 2 more" → "Pick 1 more" → "Done!"
  • The output screen after the demo is the viral moment — design it to be shareable

Key principle: The user must DO something, not just watch. And they must get something back — a result, a list, a plan, a score. This creates the sunk cost that drives conversion at the paywall.

Screen 12: VALUE DELIVERY + VIRAL MOMENT [REQUIRED]

Objective: Show the tangible output from their demo interaction. This is the thing they created, and it should be impressive enough that they'd want to share it or keep it.

  • Processing animation first: "[Generating/Building] your [output]..."
  • Then reveal the output (list, plan, summary, result)
  • Include a share button or "Send to a friend" option — this is the virality hook
  • The output should feel real, not placeholder

Key principle: The output is gated behind the next screen (account creation or paywall). The user has invested time and created something — now they need to sign up to keep it. This is ethical because the app genuinely delivers this value; the onboarding just gave them a preview.

Screen 13: ACCOUNT CREATION [OPTIONAL — based on user preference]

Objective: Soft gate — "Create a free account to unlock [the thing you just made]"

  • Show thumbnails of what they created/selected
  • "Create Free Account to unlock your [output]"
  • List 1-2 additional features they get with an account
  • Sign-in options: Apple, Google, email
  • "Already have an account? Log in" at bottom
  • Skip option if the app allows anonymous usage
Screen 14: PAYWALL [REQUIRED]

Objective: Convert to paid subscriber.

  • App logo + headline restating the transformation: "Your [goal] sorted with [App Name]"
  • One featured testimonial/review with star rating
  • Pricing: trial period + annual price
  • "Start your FREE [trial period]" primary CTA
  • "More options" or "Restore purchases" secondary link
  • If no paywall exists in the app, generate a placeholder paywall view with TODO comments for the user to connect to their payment system

Step 2: Present the Blueprint

Present the full screen sequence as a numbered list, showing:

  • Screen number
  • Screen type (from archetypes above)
  • Specific headline/question for THIS app
  • What it contains (brief)
  • Which archetype screens were skipped and why

Ask the user to confirm, reorder, add, or remove screens.

Save to memory: confirmed blueprint with screen sequence.


PHASE 4: SCREEN CONTENT

For each screen in the confirmed blueprint, draft the full content:

  • Headline (bold, short, action-oriented)
  • Subheadline (if needed — one line of supporting text)
  • Options/items (with emoji icons where appropriate)
  • CTA button text
  • Any stats or social proof copy

Present screen-by-screen. Get confirmation or iterate with the user before moving on to the next screen. Group related screens (e.g., all questionnaire screens) for efficiency.

Key content principles:

  • Write like a human, not a marketer. Short sentences. No jargon.
  • Every headline should pass the "would I say this to a friend?" test
  • Options should use the user's language, not technical terms
  • Stats should feel specific and credible — round numbers feel fake
  • CTAs should describe what happens next: "Pick my first [items]" not "Continue"

Save to memory: confirmed screen content for each screen.


PHASE 5: IMPLEMENTATION

Build the onboarding flow in the user's app.

Step 1: Understand the Codebase Architecture

Before writing any code, understand:

  • Framework and UI toolkit (SwiftUI, UIKit, React Native, Flutter, Jetpack Compose, etc.)
  • Navigation pattern (NavigationStack, UINavigationController, React Navigation, etc.)
  • Existing onboarding code (if any — extend or replace?)
  • Design system (colours, fonts, component library, spacing conventions)
  • State management approach
  • How the app currently handles first-launch detection
Step 2: Build Screen by Screen

For each screen in the blueprint:

  1. Create the view/screen following the app's existing code patterns and conventions
  2. Wire up navigation — screens should flow forward with back button support
  3. Add the progress bar — shows position in the total flow
  4. Store user responses — questionnaire answers should be persisted (these inform personalisation and can be sent to analytics)
  5. Implement interactions — tinder swipes, grid selection, multi-select checkboxes, etc.
Step 3: Build the App Demo Screen

This is the hardest screen. Approach:

  1. Identify the core UI component from the app that will be used in the demo
  2. Create a simplified version that works standalone (no dependencies on app state the user hasn't created yet)
  3. Feed it with sample/curated data that matches the user's questionnaire preferences
  4. Implement the interaction (swipe, tap, select) with a clear completion target ("Pick 3")
  5. Generate the output from their selections
  6. The output view should include a share mechanism (share sheet / export)
Step 4: Connect the Paywall
  • If the app has an existing paywall, link to it from the final onboarding screen
  • If no paywall exists, create a placeholder paywall view with:
    • Layout matching the blueprint
    • TODO comments where subscription logic would go
    • Clearly marked placeholder pricing
Step 5: Wire Up First-Launch Detection
  • Add logic so the onboarding only shows on first launch (or when reset)
  • Store completion state appropriately for the platform (UserDefaults, SharedPreferences, AsyncStorage, etc.)
  • Ensure the app launches into onboarding when state is fresh, and into the main app when complete

Save to memory: implementation progress — which screens are built, file paths, any issues.


IMPORTANT GUIDELINES

For the Questionnaire Questions
  • Questions must feel natural and conversational, not like a survey
  • Each question should make the user think "yes, they get me"
  • Options should cover the major user segments without being exhaustive
  • Use emoji icons to make lists feel lighter and more scannable
  • The order matters: start with aspiration (goals), then pain, then proof, then preference
For the App Demo
  • Keep it to ONE core interaction — don't try to demo the whole app
  • The interaction should take 30-60 seconds maximum
  • It must produce a visible, tangible result
  • That result is the viral moment — design it to be worth sharing
  • Use real data from the app's models where possible, sample data where necessary
For Copy and Tone
  • Write for someone who has never heard of the app
  • Use "you" and "your" — it's their experience, not yours
  • Headlines: bold, short, transformation-focused
  • Body text: conversational, specific, no filler
  • Stats: specific numbers feel more credible than round ones (83% > 80%)
For the Paywall
  • The paywall should feel like a natural conclusion, not an ambush
  • By this point the user has: identified their goal, felt understood, seen proof, configured preferences, and used the app — the paywall is just the final step
  • Always include a trial period option
  • Show one strong testimonial — social proof at the moment of purchase decision

© adamlyttleapps, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in the repository root of adamlyttleapps/claude-skill-app-onboarding-questionnaire.

  • SKILL.md
  • CLAUDE.md
  • LICENSE
  • README.md

Open the folder on GitHubat commit 5bc4786

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in adamlyttleapps/claude-skill-app-onboarding-questionnaire, which our catalogue first saw on October 7, 2026.

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Categories

Questions about App Onboarding Questionnaire

What does App Onboarding Questionnaire do?

Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps. App Onboarding Questionnaire is an agent skill from adamlyttleapps/claude-skill-app-onboarding-questionnaire. Design and build a high-converting questionnaire-style onboarding flow for your app, modelled on proven conversion patterns from top subscription apps.

When should I use App Onboarding Questionnaire?

App Onboarding Questionnaire fits situations like: agent Workflows work in your project.

How do I install App Onboarding Questionnaire in Claude Code?

Run `npx skills add adamlyttleapps/claude-skill-app-onboarding-questionnaire --skill app-onboarding-questionnaire -a claude-code`. Or copy the skill folder (the adamlyttleapps/claude-skill-app-onboarding-questionnaire repository) into .claude/skills/app-onboarding-questionnaire in your project. Claude Code loads it when a task matches its description.

How do I install App Onboarding Questionnaire in Codex?

Run `npx skills add adamlyttleapps/claude-skill-app-onboarding-questionnaire --skill app-onboarding-questionnaire -a codex`. Or copy the skill folder (the adamlyttleapps/claude-skill-app-onboarding-questionnaire repository) into .agents/skills/app-onboarding-questionnaire in your project. Codex loads it when a task matches its description.

Can I use App Onboarding Questionnaire in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add adamlyttleapps/claude-skill-app-onboarding-questionnaire --skill app-onboarding-questionnaire -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/app-onboarding-questionnaire, .gemini/skills/app-onboarding-questionnaire, .github/skills/app-onboarding-questionnaire and .opencode/skills/app-onboarding-questionnaire in your project.

What does App Onboarding Questionnaire need to run?

SKILL.md names no scripts, command-line tools or credentials: App Onboarding Questionnaire is instructions for the agent only.

Does App Onboarding Questionnaire access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is App Onboarding Questionnaire safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does App Onboarding Questionnaire use?

App Onboarding Questionnaire is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does App Onboarding Questionnaire use?

About 5.5k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to App Onboarding Questionnaire?

Skills that share tags, products or a category with App Onboarding Questionnaire: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains App Onboarding Questionnaire?

adamlyttleapps (a GitHub user) maintains it in adamlyttleapps/claude-skill-app-onboarding-questionnaire, which has 1,215 GitHub stars. The repository was last updated on April 6, 2026.

Source: adamlyttleapps/claude-skill-app-onboarding-questionnaire on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.