anomaly-characterization
This Claude Code skill systematically describes and classifies anomalous phenomena that cannot be explained by existing theories, providing precise starting points for abductive reasoning. Use it when you have concrete anomalous observations with a clear reference baseline and need to quantify deviations, eliminate trivial explanations, and categorize the anomaly type before pursuing novel theoretical explanations.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/anomaly-characterization && cp -r /tmp/anomaly-characterization/skills/anomaly-characterization ~/.claude/skills/anomaly-characterizationSKILL.md
# 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
```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>Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
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