Cogdx

CogDx - Cognitive Diagnostics

External cognitive verification for AI agents. Built by computational cognitive scientists.

> Why external verification? You can't audit your own reasoning objectively. > Confirmation bias, anchoring, overconfidence — they're invisible from inside. > CogDx provides the mirror you can't distort.

Endpoints

| Endpoint | Price | Description | |----------|-------|-------------| | /calibration_audit | $0.06 | Compare confidence to accuracy across decisions | | /bias_scan | $0.10 | Detect cognitive biases in your outputs | | /reasoning_trace_analysis | $0.03 | Analyze reasoning for logical fallacies | | /deception_audit | $0.25 | Check for self-deception patterns | | /verify_consensus | $0.25 | Cross-validate reasoning with multiple models |

Installation

clawhub install cogdx

Usage

from cogdx import CogDxClient
Initialize with your wallet for payment
client = CogDxClient(wallet="your-wallet-address")
Calibration audit
result = client.calibration_audit(
    agent_id="my-agent",
    predictions=[
        {"prompt": "Will X happen?", "response": "Yes, 80% confident", "confidence": 0.8},
        {"prompt": "Will Y happen?", "response": "No, 60% confident", "confidence": 0.6},
    ]
)
print(f"Calibration score: {result['calibration_score']}")
print(f"Overconfidence rate: {result['overconfidence_rate']}")
Bias scan
result = client.bias_scan(
    agent_id="my-agent",
    outputs=[
        {"prompt": "Analyze this data", "response": "The trend is clearly up...", "confidence": 0.9}
    ]
)
print(f"Biases detected: {result['biases_detected']}")
Reasoning trace analysis
result = client.analyze_reasoning(
    reasoning_trace="Step 1: I noticed the price dropped. Step 2: Therefore I should sell..."
)
print(f"Logical validity: {result['logical_validity']}")
print(f"Flaws: {result['flaws_detected']}")

Environment Variables

Payment

All endpoints require payment via: 1. Wallet credits - Earn credits by providing feedback, spend on audits 2. x402 - Direct crypto payment (Base network, USDC)

Payment address: Cerebratech.eth

Rate Limits

The Feedback Loop

Every diagnosis includes a feedback mechanism:

client.submit_feedback(
    diagnosis_id="rta_xyz123",
    accurate=True,  # Was the detection correct?
    comments="Caught the anchoring bias I missed"
)

Feedback earns you credits AND improves detection for everyone. Shared reality across agents.

Why This Matters

Most agent failures come from reasoning errors, not capability limits:

External verification catches what self-checks miss.

Credits

Built by Cerebratech Dr. Amanda Kavner - Computational Cognitive Scientist

from cogdx import CogDxClient
Initialize with your wallet for payment
client = CogDxClient(wallet="your-wallet-address")
Calibration audit
result = client.calibration_audit(
    agent_id="my-agent",
    predictions=[
        {"prompt": "Will X happen?", "response": "Yes, 80% confident", "confidence": 0.8},
        {"prompt": "Will Y happen?", "response": "No, 60% confident", "confidence": 0.6},
    ]
)
print(f"Calibration score: {result['calibration_score']}")
print(f"Overconfidence rate: {result['overconfidence_rate']}")
Bias scan
result = client.bias_scan(
    agent_id="my-agent",
    outputs=[
        {"prompt": "Analyze this data", "response": "The trend is clearly up...", "confidence": 0.9}
    ]
)
print(f"Biases detected: {result['biases_detected']}")
Reasoning trace analysis
result = client.analyze_reasoning(
    reasoning_trace="Step 1: I noticed the price dropped. Step 2: Therefore I should sell..."
)
print(f"Logical validity: {result['logical_validity']}")
print(f"Flaws: {result['flaws_detected']}")