Skill: drkavner/cogdx (version 1.1.0)

--- name: cogdx description: Cognitive diagnostics for AI agents. Calibration audits, bias detection, reasoning verification, and consensus building. Feedback builds shared reality across agents. metadata: author: "Cerebratech" version: "1.1.0" displayName: "CogDx - Cognitive Diagnostics" difficulty: "beginner" --- # 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 ```bash clawhub install cogdx ``` ## Usage ```python 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: 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 - Free tier: 100 calls/day, 2000 calls/month per wallet - Paid tier: No limits ## The Feedback Loop Every diagnosis includes a feedback mechanism: ```python 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](https://api.cerebratech.ai) Dr. Amanda Kavner - Computational Cognitive Scientist


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)