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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Validating Evaluators (Python)
Validate LLM evaluators against human-labeled examples. Target >80% TPR/TNR/Accuracy.
Calculate Metrics
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(human_labels, evaluator_predictions))
cm = confusion_matrix(human_labels, evaluator_predictions)
tn, fp, fn, tp = cm.ravel()
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)
print(f"TPR: {tpr:.2f}, TNR: {tnr:.2f}")
Correct Production Estimates
def correct_estimate(observed, tpr, tnr):
"""Adjust observed pass rate using known TPR/TNR."""
return (observed - (1 - tnr)) / (tpr - (1 - tnr))
Find Misclassified
# False Positives: Evaluator pass, human fail
fp_mask = (evaluator_predictions == 1) & (human_labels == 0)
false_positives = dataset[fp_mask]
# False Negatives: Evaluator fail, human pass
fn_mask = (evaluator_predictions == 0) & (human_labels == 1)
false_negatives = dataset[fn_mask]
Red Flags
- TPR or TNR < 70%
- Large gap between TPR and TNR
- Kappa < 0.6