to-prompt

Generate context-rich prompts for LLMs to fix or implement.

2.5k|134|Updated Mar 28, 2026
One-click install
npx skills add https://github.com/compozy/compozy --skill to-prompt-compozy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: to-prompt
Source: https://github.com/compozy/compozy/tree/main/.agents/skills/to-prompt
Command: npx skills add https://github.com/compozy/compozy --skill to-prompt-compozy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Transform code, issues, or context into detailed prompts for external LLM assistance. The goal is to provide all necessary context about the problem, current state, and requirements without prescribing an implementation approach.

Core Features & Use Cases

  • Generate exhaustive, context-rich prompts from code, issues, or project briefs.
  • Preserve problem definition, constraints, and success criteria while letting the receiving LLM decide how to implement.
  • Use cases include bug fixes, feature requests, and design explorations where precise framing is critical.

Quick Start

Describe the task context you want an external LLM to address and let it generate a comprehensive prompt.

Frequently Asked Questions about to-prompt

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

FAQPage Schema
How do I generate LLM prompts from code and project context for bug fixes?

Generating context-rich LLM prompts involves providing project context, issues, and requirements as input. The system outputs comprehensive prompts containing the full problem description and constraints, explicitly excluding implementation steps to let the receiving LLM decide the approach.

What is the best way to frame feature requests for an external LLM without prescribing implementation?

The best way to frame feature requests is to transform project briefs into detailed prompts that preserve problem definitions and success criteria. This ensures the receiving LLM understands the requirements without being constrained by prescribed implementation steps.

Can I use this to create detailed prompts for design explorations from existing issues?

Yes, you can create detailed prompts for design explorations from existing issues. The system transforms issues into exhaustive, context-rich prompts that define the problem and constraints, enabling precise external analysis without dictating the design implementation.

Why does my generated prompt exclude implementation steps for bug fixes?

Generated prompts exclude implementation steps to ensure the receiving LLM independently determines the bug-fix approach. The goal is to provide exhaustive context about the problem and constraints without prescribing how the fix should be implemented.