A sophisticated analyzer that detects when multiple AI validators share training data or architectures, leading to redundant rather than independent security checks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install validator-correlated-judgment
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install validator-correlated-judgment using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Validator Correlated Judgment?
The validator-correlated-judgment skill addresses a critical failure mode in AI security: the assumption that organizational independence equals epistemic independence. Even when validators are run by different entities, they often share underlying base models, training datasets, or fine-tuning pipelines. This creates systematic blind spots where a single evasion technique can bypass every validator simultaneously.
By integrating this tool into your workflow for Openclaw Skills, you can verify if a multi-validator attestation actually provides increased coverage or merely repeats the same check under different names. Version 1.1 introduces evaluation trace correlation, allowing users to detect similarities in reasoning patterns even when a validator's internal architecture remains undisclosed.
Validator Correlated Judgment Use Cases
- Auditing multi-validator security stacks to ensure true diversity of judgment.
- Evaluating candidate validators before adding them to an attestation registry.
- Detecting hidden model lineages in third-party validators through behavioral analysis.
- Quantifying the risk of systematic evasion across a specific set of Openclaw Skills.
- Normalizing attestation scores based on the effective number of independent validators.
How Validator Correlated Judgment Works
- Data Collection: The skill ingest validator metadata, training provenance (if available), and attestation results from multiple sources.
- Provenance Mapping: It compares base models and fine-tuning datasets to identify known architectural overlaps.
- Behavioral Correlation: The tool runs edge-case tests to see if validators agree on ambiguous results at rates higher than an independent baseline.
- Trace Analysis: For validators that publish reasoning chains, the skill analyzes the structural similarity of the logic paths used to reach a verdict.
- Verdict Generation: It calculates an Effective Independent Validator Count and provides a verdict ranging from INDEPENDENT to MONOCULTURE.
Validator Correlated Judgment Setup
To use this analyzer with your Openclaw Skills environment, ensure you have the necessary system dependencies installed.
# Ensure system dependencies are met
sudo apt-get install curl python3
# Clone or download the validator-correlated-judgment skill to your local repository
# No additional environment variables are required for basic analysis
Validator Correlated Judgment Data Schema & Taxonomy
The skill generates a detailed correlation report organized as follows:
| Attribute | Description |
|---|---|
provenance_overlap |
Assessment of shared base models and datasets |
behavioral_coefficient |
Observed agreement rate vs. predicted independent baseline |
trace_similarity_score |
Structural overlap in reasoning chains (v1.1) |
evasion_transfer_rate |
Probability that a failure in one validator transfers to another |
effective_count |
The adjusted number of unique checks provided (e.g., 2.1 instead of 3) |
verdict |
Categorization: INDEPENDENT, WEAKLY-CORRELATED, CORRELATED, or MONOCULTURE |
Validator Correlated Judgment Advanced Features
- Trace-Based Detection: Infers correlation from behavioral patterns even when provenance is undisclosed.
- Evasion Transferability Estimation: Predicts the likelihood of total system failure based on a single successful evasion.
- Custom Baseline Calibration: Adjusts the independent agreement baseline based on the difficulty of the evaluated skill set.
- Attestation Root Integration: Works alongside organizational diversity analyzers to provide a 360-degree view of trust.
SKILL.md
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