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
COGDX_WALLET- Required. Your wallet address for credit-based payment.
Payment
All endpoints require payment via:
- Wallet credits - Earn credits by providing feedback, spend on audits
- x402 - Direct crypto payment (Base network, USDC)
Payment address: Cerebratech.eth
Rate Limits
- Free tier: 100 calls/day, 2000 calls/month per wallet
- Paid tier: No 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:
- Anchoring on first information seen
- Confirmation bias in research
- Overconfidence on weak signals
- Sunk cost in bad positions
External verification catches what self-checks miss.
Credits
Built by Cerebratech Dr. Amanda Kavner - Computational Cognitive Scientist