| 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" |
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
