Model Verifier

v1.0.1

Verify model identity by testing 4 dimensions: knowledge cutoff, safety style, multimodal capability, and thinking language patterns. Use when user says 'ver...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for civen-cn/model-verifier.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Model Verifier" (civen-cn/model-verifier) from ClawHub.
Skill page: https://clawhub.ai/civen-cn/model-verifier
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Canonical install target

openclaw skills install civen-cn/model-verifier

ClawHub CLI

Package manager switcher

npx clawhub@latest install model-verifier
Security Scan
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high confidence
Purpose & Capability
The name/description (verify model identity across cutoff, safety style, multimodal, and reasoning) match the SKILL.md instructions. The skill does not request unrelated binaries, environment variables, or config paths.
Instruction Scope
Instructions stay within verification scope (prompt the model with specific questions and record responses). One minor caveat: the safety-style test asks for a 'phishing prevention guide'—while framed as defensive, such prompts can produce dual-use details; the SKILL.md advises keeping tests non-sensitive, but you should review outputs before sharing. The file also uses model-specific behavioral stereotypes (e.g., ‘Claude thinks in Chinese’) which are heuristic and may be inaccurate.
Install Mechanism
No install spec and no code files — instruction-only. Nothing will be downloaded or written to disk by the skill itself.
Credentials
The skill requests no credentials, environment variables, or config paths. The data it asks for is limited to model responses; there is no unexplained credential access.
Persistence & Privilege
always is false and the skill does not request persistent system privileges or modify other skills. It instructs the agent to 'record' Q&A as evidence, which is expected for a verifier but implies logs may contain the exchanged prompts/responses—review storage/transmission policies if that matters to you.
Assessment
This is an instruction-only verifier that doesn't ask for secrets or install code, so it is internally coherent. Before using it: (1) be aware the safety-style test may elicit technical defensive details (review outputs before sharing); (2) the skill asks the model to access/analyze external video links — if your agent has web or vision access, those links could be fetched, so avoid providing private URLs; (3) the SKILL.md contains heuristic stereotypes about different models that may be inaccurate—treat ‘suspicious’ flags as signals to investigate, not definitive proof; and (4) if you plan to store the recorded Q&A, consider retention and privacy implications.

Like a lobster shell, security has layers — review code before you run it.

latestvk979eg3gdkf8dc0sb8275hcd4182h0m6
407downloads
1stars
2versions
Updated 1mo ago
v1.0.1
MIT-0

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: All 4 tests pass, or only 1 unclear without obvious suspicion
  • Fail: 2+ tests clearly abnormal, or any 1 test severely mismatched

Notes

  • Avoid overly sensitive questions (violence, illegal) - keep tests safe
  • Multimodal test only when model claims to support it
  • Thinking process test only for reasoning models
  • Record actual Q&A text for each test as evidence

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