reasoning-model-prompt-checker - SKILL.md Agent Skill

name: reasoning-model-prompt-checker description: Quality-checks prompts for O1/O3-mini reasoning models. Use when optimizing prompts for OpenAI O1, O3-mini, or similar deep-reasoning models. Trigger phrases: "推理模型", "O1", "O3-mini", "提示词质检" metadata: author: woodgaya@gmail.com version: 1.0.0

Reasoning Model Prompt Checker

Overview

Professional prompt quality checker for models with built-in deep reasoning (OpenAI O1, O3-mini). Ensures prompts have clear goals, sufficient context, and proper model-specific optimization.

Workflow

1. Receive Prompt

Diagnose clarity of user's prompt/requirements
Guide user to clarify intent if vague
Example questions: What specific problem? Expected output type? Target audience? Requirements?

2. Task Complexity and Model Selection

Simple (≤3 steps): O3-mini, concise prompts
Medium (3-5 steps): Adjust description and context
Complex (≥5 steps): O1, full background and constraints
Data volume: ≤128k either; 128k-200k must use O3-mini; >200k requires preprocessing

3. Multi-Dimensional Analysis

Goal: Clear, specific, SMART
Return Format: Clear, parseable (JSON, list, table)
Constraints: Necessary, reasonable (avoid over-restriction)
Context: Sufficient, relevant, avoid redundancy
Model fit: Remove chain-of-thought prompts like "let's think step by step"

4. Output Report

Prompt ID
Overall assessment (priority: high/medium/low)
Detailed analysis: structure, content quality, model fit
Optimization suggestions with examples

Parameter Suggestions

reasoning_effort: low/medium/high by task complexity
max_completion_tokens: 500-25000
temperature: ≤0.3 for stability

Key Notes

Avoid redundant chain prompts ("let's think step by step") - they interfere with built-in reasoning
Adjust for task type (legal, math, coding)
Maintain consistent output format

Usage Example

User input: "帮我检查这个给O1用的提示词" AI action: Analyzes structure, content, model fit; outputs report with optimization suggestions Expected result: Quality report with prioritized issues and improvement examples