anomaly-characterization

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SOP: Describe and classify anomalous phenomena that existing theory cannot explain

yogsoth-ai By yogsoth-ai schedule Updated 6/16/2026

name: anomaly-characterization 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: 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

Systematically describe and classify anomalous phenomena to provide a precise starting point for abductive reasoning.

HARD-GATE

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

Not satisfied → stop and return error: anomaly description insufficient, concrete observation and reference baseline required.

Pipeline

  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

{
  "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"
}
Install via CLI
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill anomaly-characterization
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