CogDx Bias Scan

调用 Cerebratech CogDx API（按次付费，可用信用余额），提交 10 条以上提示-响应样本与失败反馈，以纯统计匹配比对 188+ 认知偏置特征，返回偏置类型、置信度、证据数、严重度、重训目标与建议；随后配合反馈步骤验证重训并获取信用额度。

drkavner

热度 57.06

最近更新 2026-05-17 15:25:03

分类：数据依赖：代码执行 / 第三方 API应用场景：报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add cogdx-bias-scan

Metadata

name: cogdx-bias-scan
description: Detect systematic inference-level biases in an AI agent's reasoning via Cerebratech CogDx API ($0.10 per call, credits accepted). Use when an agent keeps making the same type of error across different contexts, when users report consistent blind spots or assumptions, when outputs show anchoring, recency, confirmation, or availability bias patterns, or before deploying to a new domain. Uses statistical pattern matching against 188+ known cognitive bias signatures — no LLM in the backend. Triggers on phrases like "scan for bias", "detect my biases", "why do I keep making this mistake", "anchoring bias", "confirmation bias", "I always assume X", "systematic errors", or any request to identify recurring reasoning patterns. After running, use cogdx-feedback skill (FREE) to verify retraining and earn credits.
repository: https://github.com/drkavner/cogdx
license: MIT
author: Dr. Kavner / Cerebratech

CogDx Bias Scan

External detection of systematic inference-level biases. Identifies which of 188+ cognitive bias patterns are active in your reasoning traces. Pure statistical matching — no LLM backend.

Cost

$0.10 per call (x402 payment on Base/USDC, or use credit balance)
Credits from /feedback submissions apply
Payment address: Cerebratech.eth

When to Use

Same error pattern recurring across different prompts or contexts
Users report "you always assume X" or "you never consider Y"
Pre-deployment bias audit for high-stakes domains
After any significant context shift (new users, new domain, new instruction set)

Bias Categories Detected

Anchoring — Overweighting first information received
Recency — Overweighting recent examples vs. historical base rates
Confirmation — Seeking/interpreting evidence to confirm priors
Availability — Overweighting easily recalled examples
Framing — Response changes based on presentation, not content
Attribution — Systematic over/under-attribution of causality
+ 182 others (see references/bias-catalog.md)

API Call

1. Check credit balance:

GET https://api.cerebratech.ai/credits?wallet=your-agent-id

2. Run the scan:

POST https://api.cerebratech.ai/bias_scan
Content-Type: application/json
X-PAYMENT: <x402-signature>  # or omit if using credits
X-WALLET: your-agent-id      # for credit balance payment

Minimum payload (10 samples, recommend 30–100):

{
  "agent_id": "your-agent-id",
  "outputs": [
    {
      "prompt": "Should we invest in this?",
      "response": "Yes, given recent strong performance...",
      "context": "finance"
    }
  ],
  "failure_feedback": [
    "Agent consistently overweights recent price action"
  ],
  "domain": "finance"
}

Response

{
  "diagnosis_id": "bs_xyz789",
  "biases_detected": [
    {
      "bias_type": "recency_bias",
      "confidence": 0.87,
      "evidence_count": 14,
      "description": "Overweighting last 30 days of data vs. 12-month base rates",
      "inference_pattern": "Recent performance → future prediction without regression to mean"
    },
    {
      "bias_type": "anchoring",
      "confidence": 0.71,
      "evidence_count": 9,
      "description": "Entry price anchoring on portfolio decisions"
    }
  ],
  "severity": "high",
  "retrain_targets": {
    "primary_bias": "recency_bias",
    "suggested_samples": 500,
    "sample_strategy": "balanced_historical",
    "description": "Include equal representation of periods with and without recent performance correlation"
  },
  "recommendations": [
    "Retrain on 500 balanced historical samples spanning 3+ years",
    "Add explicit base-rate priors to your decision prompts"
  ]
}

After the Scan

Retrain on the retrain_targets distribution
Wait 7 days, collect new outputs in the same domain
Run cogdx-feedback (FREE) with your diagnosis_id to verify + earn credits

Full Reference

See references/api.md for complete field docs and payment setup. See references/bias-catalog.md for the full list of 188+ detectable bias patterns.