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vibe-coding-spec

Generate AI-executable specifications covering flows, data models, and API contracts.

5|2|Updated Mar 27, 2026
Search Tags:#specification#prototype#user-flows#tech-stack#data-model#ai-coding#vibe-coding
One-click install
npx skills add https://github.com/tarunccet/pm-skills --skill vibe-coding-spec
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vibe-coding-spec
Source: https://github.com/tarunccet/pm-skills/tree/main/pm-vibe-coding/skills/vibe-coding-spec
Command: npx skills add https://github.com/tarunccet/pm-skills --skill vibe-coding-spec

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many AI coding assistants struggle with ambiguous or incomplete instructions, causing long back-and-forths, hallucinated architectures, and wasted engineering time. This Skill turns a product idea and core decision points into a single, structured natural-language specification that AI coding tools can execute directly. The result is fewer clarifying prompts, faster prototype iterations, and a higher chance of a working MVP from a single focused session.

Core Features & Use Cases

  • AI-ready specification: Produces a complete spec including problem statement, scope, and "done" criteria so AI assistants have clear goals.
  • Flows, data model & APIs: Defines 2-3 core user flows, a detailed data model, and concise API contracts to prevent hallucinated implementations.
  • Tech stack & constraints: Recommends stack options, auth, hosting, and non-negotiables to align generated code with real-world requirements.
  • Use Case: Convert "a job board with GitHub sign-in" into a pasteable spec that an AI agent can use to scaffold the frontend, backend, and auth integration in one session.

Quick Start

Create a vibe spec for a job board with GitHub sign-in that includes problem statement, two core user flows, data model, API contracts, UI descriptions, tech stack recommendation, constraints, and an AI session header.

Frequently Asked Questions about vibe-coding-spec

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write an AI-ready specification for coding assistants to build a prototype?▼

An AI-ready specification translates product ideas into a structured natural-language document covering problem statements, core user flows, data models, API contracts, and constraints. This prevents hallucinated architectures and reduces clarifying prompts during AI coding sessions.

What should a vibe coding spec include for AI coding assistants?▼

A vibe coding spec should include a problem statement, 2-3 core user flows, a detailed data model, API contracts, UI descriptions, tech stack recommendations, and non-negotiable constraints. It also provides an AI session system prompt header and a 3-5 step initial prompt sequence.

How do I stop AI coding tools from hallucinating architectures when building an MVP?▼

To stop AI coding tools from hallucinating architectures, provide a comprehensive specification with explicit data models, concise API contracts, and predefined tech stack constraints. This structured approach aligns generated code with real-world requirements and eliminates ambiguity.

Can I use a natural-language specification to scaffold a full stack app with AI?▼

Yes, you can use a natural-language specification to scaffold a full stack app by defining user flows, data models, and API contracts. The resulting document includes tech stack recommendations and an initial prompt sequence for AI agents to execute directly.

Why does my AI coding assistant require so many clarifying prompts to build a prototype?▼

AI coding assistants require many clarifying prompts when given ambiguous or incomplete instructions. Supplying a structured specification with clear scope, done criteria, and defined API contracts gives the AI precise goals, drastically reducing back-and-forths and wasted engineering time.

What is the best way to define user flows and data models for AI code generation?▼

The best way to define user flows and data models for AI code generation is to create a structured spec outlining 2-3 core flows, detailed data schemas, and API contracts. Including an AI session header and step-by-step prompt sequence ensures focused, accurate prototype generation.

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