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/anomaly-characterization

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

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill anomaly-characterization --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/anomaly-characterization

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

anomaly-characterization.SKILL.md
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

<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. </HARD-GATE>

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"
}

</output>

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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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