formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced…
SOP: Describe and classify anomalous phenomena that existing theory cannot explain
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill anomaly-characterization --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/anomaly-characterizationContext preview
The summary Claude sees to decide when to auto-load this skill.
SOP: Describe and classify anomalous phenomena that existing theory cannot explain
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
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. </HARD-GATE>
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
{
"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>
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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