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cogdx-bias-scancogdx 偏差扫描

Agent Skill

cogdx-bias-scan 用于补充运维相关能力,适合在 OpenClaw 中需要让 Agent 承接运维相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cogdx-bias-scan(cogdx 偏差扫描)
来源仓库:https://github.com/drkavner/cogdx-bias-scan
安装命令:
openclaw skills install cogdx-bias-scan
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install cogdx-bias-scan

简介

Cogdx-bias-scan 通过 Cerebratech API 检测 AI 代理推理中的系统级偏差。

  • 每次调用收费 0.10 美元,接受信用额度支付。
  • 适合需要定期审计模型公平性与一致性的运维场景。
  • 在 OpenClaw 中通过 clawhub 安装,需启用 make 编译支持。
  • 使用前应限制调用频率,防止费用超支或 API 限流。

SKILL.md

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

  1. Retrain on the retrain_targets distribution
  2. Wait 7 days, collect new outputs in the same domain
  3. 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.

适合场景

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能力 5

展示第三方安全扫描或审计结果

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平台分布

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