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ai-spec

Convert natural language requirements into structured technical specifications and AI-executable coding instructions.

6|1|Updated Jul 1, 2026
Search Tags:#workflow#architecture#specification#engineering#system-design#ai-prompt
One-click install
npx skills add https://github.com/PancrePal-xiaoyibao/VitaForge --skill ai-spec-pancrepal-xiaoyibao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-spec
Source: https://github.com/PancrePal-xiaoyibao/VitaForge/tree/main/.gemini/skills/ai-spec
Command: npx skills add https://github.com/PancrePal-xiaoyibao/VitaForge --skill ai-spec-pancrepal-xiaoyibao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill bridges the gap between vague natural language requirements and precise, executable technical specifications, preventing development ambiguity and ensuring high-quality architectural outcomes.

Core Features & Use Cases

  • Architectural Design: Automatically generates ADRs, system designs, and directory structures based on best practices.
  • God Prompt Generation: Creates highly detailed, context-aware prompts for AI coding agents to ensure consistent implementation.
  • Workflow Enforcement: Mandates a rigorous development lifecycle including Repo Init, Checkfix loops, and completion verification.
  • Use Case: When starting a new biomedical data pipeline, use this Skill to define the API-first modular architecture, set up the directory structure, and generate the exact instructions for an AI agent to implement the core service layers.

Quick Start

Use the ai-spec skill to generate a technical specification and implementation prompt for a new Python-based scRNA-seq data processing service.

Frequently Asked Questions about ai-spec

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

FAQPage Schema
How do I turn fuzzy natural language requirements into structured technical specifications?▼

Architectural decision records (ADRs) and system designs are generated from natural language requirements by applying engineering best practices. This process automatically structures directory layouts and defines API-first modular architectures, ensuring your initial system design prevents development ambiguity before coding begins.

How do I generate AI-executable coding instructions for a new system architecture?▼

Generating AI-executable coding instructions involves creating highly detailed, context-aware prompts for AI coding agents. This enforces a rigorous development workflow—including automated checkfix loops and strict completion verification protocols—to ensure consistent implementation of your technical specifications across production-grade services.

What is the best way to enforce a rigorous engineering workflow when starting a new software service?▼

Yes, this specification generation approach handles biomedical data pipelines by defining API-first modular architectures and setting up exact directory structures. It generates precise implementation instructions for AI agents to build core service layers for complex applications like scRNA-seq data processing.

Why does my development process suffer from ambiguity when implementing new features?▼

Development ambiguity occurs when there is a gap between vague natural language requirements and precise executable technical specifications. Bridging this gap with structured system design and strict completion verification protocols prevents misinterpretation and ensures consistent architectural outcomes.

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