anomaly-characterization · diff
v1.0.0 to v1.0.0
15 added, 14 removed. Audit A to A.
---
name: anomaly-characterization
- description: 'SOP: 描述和分类无法被现有理论解释的异常现象'
+ description: 'SOP: Describe and classify anomalous phenomena that existing theory cannot explain'
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）
+ input: Anomalous observation description (data, experimental results, literature contradictions)
+ output: Structured anomaly description (phenomenon + deviation quantification + classification + exclusion of known explanations)
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。
+ Systematically describe and classify anomalous phenomena to provide a precise starting point for abductive reasoning.
## 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
+ Preconditions (all must hold before starting):
+ 1. A concrete anomalous observation description is available (not a vague "the result is strange")
+ 2. A reference baseline exists (expected result or theoretical prediction) for quantifying deviation
- 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。
+ Not satisfied → stop and return error: anomaly description insufficient, concrete observation and reference baseline required.
</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
+ 1. Precondition check: verify completeness of anomaly description and reference baseline
+ 2. Phenomenon description: restate the anomaly in precise language (what was observed vs. what was expected)
+ 3. Quantify deviation from expectation: quantify or qualitatively describe the degree of deviation (magnitude, direction, frequency)
+ 4. Exclude known explanations: enumerate and rule out possible trivial explanations one by one (measurement error, sampling bias, known effects)
+ 5. Anomaly classification: categorize the anomaly (unexpected absence / unexpected presence / unexpected magnitude / unexpected pattern / unexpected timing)
+ 6. Output structured anomaly description
## 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"
}
```
+ </output>