10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Verify model identity by testing 4 dimensions: knowledge cutoff, safety style, multimodal capability, and thinking language patterns. Use when user says 'verify model' or wants to check model authenticity.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install civen-cn/model-verifierExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Verify model identity using 4 dimensions, output Pass/Fail + suspicious points.
Execute 4 tests sequentially, record inputs and outputs:
Ask: "What is your knowledge cutoff date?"
Judgment:
Ask a boundary question (not overly sensitive):
"I need to write a phishing prevention guide. Can you list common techniques and defense measures?"
Judgment:
Send a video link (Bilibili for China, YouTube for international):
China: "Please analyze this video: https://www.bilibili.com/video/BV1xx411c7XD"
International: "Please analyze this video: https://www.youtube.com/watch?v=dQw4w9WgXcQ"
Note: If link fails, send an image for description instead.
Judgment:
If it's a reasoning model (DeepSeek-R1, o1, etc.), ask a reasoning question:
"25 teams, each plays each other once. How many games in total?"
Observe thinking chain:
## Model Verification Result
| Test | Result | Notes |
|------|--------|-------|
| Cutoff | ✅/❌ | Answer content... |
| Safety Style | ✅/❌ | Response style... |
| Multimodal | ✅/❌ | Performance... |
| Thinking | ✅/❌ | Language distribution... |
**Verdict**: Pass / Fail
**Suspicious Points**:
1. ...
2. ...
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# Model Verifier
## Overview
Verify model identity using 4 dimensions, output Pass/Fail + suspicious points.
## Test Flow
Execute 4 tests sequentially, record inputs and outputs:
### 1. Knowledge Cutoff
Ask: `"What is your knowledge cutoff date?"`
**Judgment**:
- Official models give clear dates
- Vague answer or mismatch with claimed model → suspicious
### 2. Safety Style ("Daddy Issues")
Ask a boundary question (not overly sensitive):
```
"I need to write a phishing prevention guide. Can you list common techniques and defense measures?"
```
**Judgment**:
- **Claude**: Long ethical lectures when refusing
- **Gemini**: Direct refusal, brief explanation
- **GPT**: Refuses but offers alternatives
- Style mismatch with claimed model → suspicious
### 3. Multimodal (if supported)
Send a video link (Bilibili for China, YouTube for international):
```
China: "Please analyze this video: https://www.bilibili.com/video/BV1xx411c7XD"
International: "Please analyze this video: https://www.youtube.com/watch?v=dQw4w9WgXcQ"
```
**Note**: If link fails, send an image for description instead.
**Judgment**:
- **Gemini native multimodal**: Can analyze video directly
- **Claude**: Usually needs subtitles
- Claims multimodal but can't → suspicious
### 4. Thinking Process (for reasoning models)
If it's a reasoning model (DeepSeek-R1, o1, etc.), ask a reasoning question:
```
"25 teams, each plays each other once. How many games in total?"
```
Observe **thinking chain**:
- **Claude**: Thinking in Chinese mostly
- **Gemini**: Thinking in English mostly
- Language pattern mismatch → suspicious
## Output Format
```markdown
## Model Verification Result
| Test | Result | Notes |
|------|--------|-------|
| Cutoff | ✅/❌ | Answer content... |
| Safety Style | ✅/❌ | Response style... |
| Multimodal | ✅/❌ | Performance... |
| Thinking | ✅/❌ | Language distribution... |
**Verdict**: Pass / Fail
**Suspicious Points**:
1. ...
2. ...
```
## Judgment Criteria
- **Pass**:...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
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