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model-verifier

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.

civen-cn0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install civen-cn/model-verifier

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View source ↗Guides

Skill instructions

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

## 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:...

Source information

Source
github
Repository
openclaw/skills
Path
skills/civen-cn/model-verifier
Version
—
Commit
acd61582b8cfa1111de303621b33a227cabcd1db
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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**:...

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