Guides Claude from idea to working prototype using frameworks from OpenAI, Figma, and Airbnb. Use when starting new product features, planning MVP scope, making build-vs-buy decisions, or guiding users from concept to shippable prototype. Applies AI-first thinking (Kevin Weil), simplicity forcing functions (Dylan Field), and complete experience design (Brian Chesky).
npx skills add https://github.com/menkesu/awesome-pm-skills --skill zero-to-launch
Claude uses this skill when:
The Model Improvement Mindset:
> "The AI models you're using today is the worst AI model you will ever use for the rest of your life. Every two months, computers can do something they've never been able to do before."
Use when: Building any product that could benefit from AI capabilities
How:
Example:
User request: "Build a search feature"
Apply AI-First Thinking:
✅ Could AI understand intent better than keyword matching?
✅ Design for streaming results (models will get faster)
✅ Add eval: "Does search return relevant results for ambiguous queries?"
✅ Hybrid: Use AI for intent, traditional for exact matches
The Core Question:
> "The hardest thing is saying no. We operationalize simplicity by constantly asking: What's the ONE thing that matters here?"
Use when: Feature scope is unclear or growing too large
How:
Example:
User request: "Build a dashboard with 15 metrics"
Apply Simplicity Test:
❌ 15 metrics = paralysis
✅ What's the ONE metric that drives action?
✅ Show that metric prominently
✅ Hide other 14 behind "View Details"
✅ Polish the main metric display (craft quality)
The One Roadmap Philosophy:
> "We shifted to one company roadmap. Leaders are in the details. If you don't know the details, how do you know people are doing a good job?"
Use when: Building features that touch multiple parts of the product
How:
Example:
User request: "Build user onboarding"
Apply Complete Experience:
✅ Map full journey: Signup → Setup → First Value → Habit
✅ Design all states: Loading screens, errors, empty states
✅ Include: Welcome email, in-app guidance, success celebration
✅ Story: "Get your first [outcome] in under 2 minutes"
START: New Feature Idea
│
├─ Can AI 10x this? ─────────────────┐
│ YES: Apply AI-First Framework │
│ NO: Continue ↓
│ Use OpenAI Patterns
├─ What's the ONE core job? ─────────┤
│ Apply Simplicity Test │
│ Define: Must-have vs nice-to-have │
│ │
├─ Map complete experience ───────────┤
│ All states, full journey │
│ Cross-functional considerations │
│ │
└─ BUILD MVP ←───────────────────────┘
Start with core job
Add details that create moats
Ship to small group first
# Feature: [Name]
## The ONE Job (Figma Simplicity Test)
What outcome must this deliver?
- Core job: [describe]
- Success = when user can [achieve outcome]
## AI-First Considerations (OpenAI Thinking)
- Could AI help? [yes/no + how]
- Designed for future models? [yes/no]
- Evals needed: [list test cases]
## Complete Experience (Airbnb Approach)
- User journey: [list steps]
- States to design: [loading, error, empty, success]
- Story to tell: "[how we'll talk about this]"
## MVP Scope
**Must Have (Week 1):**
- [feature]
- [feature]
**Should Have (Week 2):**
- [feature]
**Nice to Have (Later):**
- [feature]
When deciding what to build:
| Question | Framework | Action |
|----------|-----------|--------|
| Is this AI-suitable? | OpenAI | Build with AI if: repetitive, pattern-matching, improves over time |
| What's essential? | Figma | Strip to ONE core job, build that first |
| Is experience complete? | Airbnb | Design all states before building any |
| Can we ship in 1 week? | All | If no, scope is too big - simplify |
Before You Code:
During Build:
Before Ship:
Challenge: Users wanted ChatGPT to remember context across conversations
AI-First Approach:
Result: Feature that gets better as models improve
Challenge: Users requested 50+ features
Simplicity Test Applied:
Result: Killer feature that defined the product
Challenge: Rebuilding entire product experience
Complete Experience Approach:
Result: Coherent product, not disconnected features
Problem: Missing 10x opportunities by defaulting to traditional approaches
Fix: Always ask: "Could AI make this 10x better?"
Problem: "Just one more feature" → bloated MVP that never ships
Fix: Ruthlessly apply Figma simplicity test - ONE core job
Problem: Shipping without error/empty/loading states
Fix: Use Airbnb complete experience checklist
Problem: Building what's requested vs solving jobs
Fix: Start with user job, not feature request
Kevin Weil (OpenAI):
> "Our general mindset is in two months, there's going to be a better model. If you're building and the product is right on the edge of capabilities, keep going. You're doing something right."
Dylan Field (Figma):
> "AI makes design, craft, and quality the new moat for startups. The bar for quality is going to go way up."
Brian Chesky (Airbnb):
> "Way too many founders apologize for how they want to run the company. What everyone really wants is clarity and to row in the same direction."
For deeper dives on specific topics, see:
Mobile-first design and engineering doctrine for iOS and Android apps. Covers touch interaction, performance, platform conventions, offline behavior, and mobile-specific decision-making. Teaches principles and constraints, not fixed layouts. Use for React Native, Flutter, or native mobile apps.
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
Design systems, plan implementations, review architecture decisions - Use when you need to plan a complex feature, design system architecture, or make high-level technical decisions.
Analyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
>- Plans, configures, and manages core GKE cluster networking. Covers private clusters, VPC-native configurations, DNS, node egress, Dataplane V2, and IP planning. Use when designing GKE networking layouts, configuring private clusters, setting up Dataplane V2, planning GKE IP ranges, or managing VPC- native cluster modes. Don't use for application ingress, load balancing, or service networking (use gke-service-networking instead).
Read this before adding or importing a component; follow the workflow instead of guessing. Explains how to add a new component to an azldev distro, covering inspecting the upstream spec, the inline-versus-dedicated-file decision, and validating with render, diff-sources, and build. Triggers include add component, new package, import package, create comp.toml, new component.
How to work in a Plain Notes project (the `plain-notes` starter pack): a flat notes/ folder plus a daily/ journal. The 'I just want to write' layout. Read when the project has these folders, OR when asked to jot a note, capture a quick thought, or write today's journal entry. Carries the linking habit and daily-entry behavior so templates and folder descriptions stay minimal. Complements the platform `open-knowledge` skill; does not replace it.
How to work in a Worldbuilding project (the `worldbuilding` starter pack): a fiction encyclopedia of characters, settings, themes, factions, and lore. Read when the project has these folders, OR when asked to add a character, setting, faction, or piece of lore, or to check the world for internal consistency. Carries the auto-stub and consistency behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.
Take menkesu/zero-to-launch from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.