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construct-superloop

Guide creation of Superloop feature specifications and generate spec.md files.

2|Updated Jan 5, 2026
Search Tags:#workflow automation#feature development#ai agent#spec generation#codebase analysis#superloop
One-click install
npx skills add https://github.com/Supergent/superloop --skill construct-superloop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: construct-superloop
Source: https://github.com/Supergent/superloop/tree/main/.claude/skills/construct-superloop
Command: npx skills add https://github.com/Supergent/superloop --skill construct-superloop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill guides users through creating detailed specifications for new features, ensuring they are well-defined for automated implementation by the Superloop AI agent.

Core Features & Use Cases

  • Guided Spec Creation: Walks users through defining requirements, acceptance criteria, and technical approaches.
  • Codebase Contextualization: Analyzes existing code to inform spec development.
  • Use Case: A developer wants to add a new API endpoint. They use this Skill to define the endpoint's functionality, expected inputs/outputs, and how it integrates with the existing codebase, resulting in a spec.md file ready for Superloop.

Quick Start

Use the construct-superloop skill to start building a new feature specification.

Frequently Asked Questions about construct-superloop

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

FAQPage Schema
How do I create feature specifications for automated AI agent workflows?▼

Create feature specifications for AI agent workflows by using a guided process to define requirements, acceptance criteria, and technical constraints. This structured approach generates a spec.md file and configures the necessary automation settings.

How do I generate a spec.md for new API endpoints using existing codebase context?▼

Generate a spec.md for new API endpoints by analyzing existing code to inform development and defining expected inputs, outputs, and integration approaches. This walks you through documenting the exact functionality needed for implementation.

What is the best way to define technical constraints for AI automated feature development?▼

The best way to define technical constraints for AI automated feature development is through a structured specification process that captures codebase context and requirements. This ensures the generated specs are well-defined for automated implementation.

Do I need to configure a specific file for AI agent workflow automation?▼

Yes, you need to configure a .superloop/config.json file to enable automated AI agent workflows. This configuration is generated alongside the feature specification to define the operational parameters for the agent.

Can I use guided spec generation for integrating new features into an existing codebase?▼

Yes, you can use guided spec generation to integrate new features into an existing codebase. The process analyzes your current code to inform spec development, ensuring the new feature matches your established technical approach.

Why does my AI agent need a structured feature specification before implementation?▼

Your AI agent needs a structured feature specification to ensure new features are well-defined for automated implementation. Without detailed requirements and codebase context, the agent cannot accurately execute the development workflow.

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