anomaly-characterization

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/anomaly-characterization

系统描述和分类异常现象,为溯因推理(abductive reasoning)提供精确的起点。

SKILL.md
.github/skills/anomaly-characterizationView on GitHub ↗
---
name: anomaly-characterization
description: "SOP: 描述和分类无法被现有理论解释的异常现象"
version: 1.0.0
category: hypothesis-formation
type: sop
campaign: hypothesis-formulation
input: "异常观察描述(数据、实验结果、文献矛盾)"
output: "结构化异常描述(现象 + 偏差量化 + 分类 + 排除已知解释)"
dependencies:
  skills:
    - subagent-spawning
---

# Anomaly Characterization
系统描述和分类异常现象,为溯因推理(abductive reasoning)提供精确的起点。

## HARD-GATE
<HARD-GATE>
前置条件(全部满足才能开始):
1. 已有具体的异常观察描述(不能是模糊的"结果很奇怪")
2. 有参照基准(预期结果或理论预测)用于量化偏差

不满足 → 停止,返回错误:异常描述不足,需要具体观察和参照基准。
</HARD-GATE>

## Pipeline
1. 前置检查:验证异常描述和参照基准完整性
2. 现象描述:用精确语言重述异常(what was observed vs. what was expected)
3. 与预期偏差量化:量化或定性描述偏差程度(magnitude, direction, frequency)
4. 排除已知解释:列举并逐一排除可能的平凡解释(测量误差、采样偏差、已知效应)
5. 异常分类:将异常归类(unexpected absence / unexpected presence / unexpected magnitude / unexpected pattern / unexpected timing)
6. 输出结构化异常描述

## Output Format
```json
{
  "anomaly_id": "A1",
  "phenomenon": "Precise description of what was observed",
  "expected": "What theory or prior evidence predicted",
  "deviation": {
    "direction": "higher | lower | absent | present | different_pattern",
    "magnitude": "Quantitative or qualitative estimate",
    "frequency": "Isolated | recurring | systematic"
  },
  "excluded_explanations": [
    {"explanation": "...", "reason_excluded": "..."}
  ],
  "anomaly_type": "unexpected_absence | unexpected_presence | unexpected_magnitude | unexpected_pattern | unexpected_timing",
  "severity": "minor | moderate | major",
  "notes": "Additional context"
}
```
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