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"
}
```
More from yogsoth-ai/de-anthropocentric-research-engine
- abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
- ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
- ablation-component-mappingMap system architecture to ablatable units for ablation studies
- ablation-designDesign ablation studies to isolate component contributions in ML systems
- ablation-executionRemove components one by one from a system, record the response/impact of each removal.
- abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
- abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
- abstraction-ladderPerform bisociation at multiple abstraction levels
- abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
- abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.