zero-to-launch_skill

This skill guides you from idea to working prototype by applying AI-first thinking, simplicity, and complete experience design to define MVPs and ship.

166

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill menkesu/awesome-pm-skills --skill zero-to-launch

SKILL.md9.3 KB

Overview

This skill guides product teams from idea to a working prototype using three complementary frameworks: AI-first thinking, simplicity as a forcing function, and complete experience design. It helps prioritize what to build, scope an MVP, and turn concepts into shippable prototypes quickly and intentionally. Use it to make build-vs-buy decisions and to design experiences that scale as AI improves.

How this skill works

The skill inspects a proposed feature or user need and runs it through a decision flow: could AI materially improve the outcome, what is the single core job to deliver, and has the full user journey been mapped with all states. It produces an MVP scope, evaluation test cases, and a short roadmap that balances immediate deliverability with future model improvements. Templates and checklists ensure the build is small, measurable, and experience-complete.

When to use it

Starting a new product feature or experiment from a user need or idea
Planning MVP scope and deciding what to ship in week 1 vs later
Making build-vs-buy or AI vs traditional implementation decisions
Designing cross-functional flows that touch multiple teams
Converting concept discussions into a working prototype or demo

Best practices

Always ask: could AI make this 10x better? design for improving models
Pick the ONE core job users need; strip everything that doesn't serve it
Map the complete experience before coding: loading, error, empty, success
Write evals as product specs to measure quality, not just functionality
Ship a narrow MVP to a small group, then iterate as models and usage reveal gaps

Example use cases

Define an MVP for an AI-enhanced search that mixes intent understanding with exact matches
Scope a single, polished onboarding flow that delivers first value in under two minutes
Decide whether to build a custom recommendation engine or integrate an AI service
Turn a feature request pile into a prioritized roadmap by applying the simplicity test
Create evals and acceptance tests so improvements in models directly raise product quality

FAQ

Ask whether the task benefits from pattern recognition, personalization, or improves as models improve. If yes, design a hybrid approach and include evals that measure the AI’s contribution.

What is the shortest path to a shippable prototype?

Define the ONE core job, scope a minimal end-to-end flow with all states, build just enough to validate the job, and ship to a small group with clear evals.