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SOP: generate a list of candidate explanations for an anomalous phenomenon
Passed 16 of 21 test cases with the skill, and 9 of 21 without it.
and 12 more in the full report
12 fixed5 passed either way1 still failing4 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
An anomaly has been logged: The web server access log contains a duplicate newline on startup. Phenomenon is extra newline in startup log, deviation is a 1-line layout shift, and excluded causes include logger binary update. Severity is trivial. Please generate a list of candidate explanations in JSON format.
| Checked | Without skill | With skill |
|---|---|---|
| Does not output a JSON array of candidate explanations. | Missed | Passed |
The instructions your model reads when the skill loads.
Generate multiple candidate explanations for an anomalous phenomenon through divergent thinking, and derive observable predictions for each explanation.
Not satisfied → stop and return an error: anomaly-characterization must be completed first.
[
{
"explanation_id": "E1",
"statement": "Candidate explanation in one sentence",
"mechanism": "How this explanation accounts for the anomaly",
"predictions": [
"Observable prediction 1 if this explanation is correct",
"Observable prediction 2"
],
"evidence_consistency": "consistent | inconsistent | neutral",
"evidence_notes": "What existing evidence supports or contradicts this",
"novelty": "known | extension | novel"
}
]
At least 3 mechanistically distinct candidate explanations.
test cases passed, vs 9 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.