reasoning-model-prompt-checker

Evaluate prompts for deep-reasoning models on clarity, structure, and fit.

7|2|Updated Aug 24, 2023

Search Tags:#reasoning #prompt-engineering #model-optimization #prompt-check #o1 #o3-mini

Ready to run

npx skills add https://github.com/pingdior/usingSkills --skill reasoning-model-prompt-checker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reasoning-model-prompt-checker
Source: https://github.com/pingdior/usingSkills/tree/main/reasoning-model-prompt-checker
Command: npx skills add https://github.com/pingdior/usingSkills --skill reasoning-model-prompt-checker

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps authors and prompt engineers identify and fix clarity, structure, and model-fit issues in prompts intended for deep-reasoning models, reducing failed outputs and unnecessary iterations.

Core Features & Use Cases

Structured Diagnosis: Evaluates goal clarity, required steps, return format, constraints, and context sufficiency.
Model Selection Guidance: Recommends when to use O1 versus O3-mini based on task complexity and data volume.
Optimization Suggestions: Provides rewrites, format examples (JSON/table/list), and parameter recommendations for stable outputs.
Use Case: Optimize a multi-step technical analysis prompt for O1 to ensure consistent JSON output and bounded token usage.

Quick Start

Analyze and optimize this prompt for O1/O3-mini to clarify intent, specify output format, and recommend parameter settings.

Frequently Asked Questions about reasoning-model-prompt-checker

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

FAQPage Schema

How do I optimize prompts for O1 and O3-mini reasoning models?▼

To optimize prompts for O1 and O3-mini reasoning models, evaluate goal clarity, specify return formats like JSON or tables, validate constraints, and adjust parameters like reasoning_effort and max_completion_tokens to ensure stable multi-step outputs.

When do I need to use a prompt checker for deep-reasoning models?▼

You need a prompt checker for deep-reasoning models when executing multi-step reasoning, technical problem solving, or format-constrained outputs, ensuring your prompt provides sufficient context and clear structural guidance to prevent failed iterations.

Should I choose O1 or O3-mini for complex technical problem solving?▼

Model selection between O1 and O3-mini depends on task complexity and data volume. Structured prompt diagnosis evaluates your specific requirements to recommend the appropriate reasoning model, ensuring the chosen model aligns with your technical analysis needs.

Why does my O1 model output fail to return consistent JSON format?▼

O1 model outputs fail to return consistent JSON format when prompts lack explicit return format specifications and bounded token usage. Prompt optimization adds format examples and parameter recommendations to constrain outputs and resolve structural inconsistencies.

What parameters should I set for O3-mini prompts requiring multi-step reasoning?▼

For O3-mini prompts requiring multi-step reasoning, you should set parameters like reasoning_effort and max_completion_tokens. Prompt optimization analyzes your task to recommend specific parameter configurations that bound token usage and stabilize deep-reasoning outputs.

Can I use this prompt checker to fix clarity and structure issues in existing prompts?▼

Yes, you can use the prompt checker to fix clarity and structure issues in existing prompts. It performs a structured diagnosis to identify insufficient context, unclear goals, and missing constraints, then provides optimization suggestions and rewrites for deep-reasoning models.