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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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Anti-Patterns
Common mistakes and fixes.
| Anti-Pattern | Problem | Fix |
|---|---|---|
| Generic metrics | Pre-built scores don't match your failures | Build from error analysis |
| Vibe-based | No quantification | Measure with experiments |
| Ignoring humans | Uncalibrated LLM judges | Validate >80% TPR/TNR |
| Premature automation | Evaluators for imagined problems | Let observed failures drive |
| Saturation blindness | 100% pass = no signal | Keep capability evals at 50-80% |
| Similarity metrics | BERTScore/ROUGE for generation | Use for retrieval only |
| Model switching | Hoping a model works better | Error analysis first |
Quantify Changes
baseline = run_experiment(dataset, old_prompt, evaluators)
improved = run_experiment(dataset, new_prompt, evaluators)
print(f"Improvement: {improved.pass_rate - baseline.pass_rate:+.1%}")
Don't Use Similarity for Generation
# BAD
score = bertscore(output, reference)
# GOOD
correct_facts = check_facts_against_source(output, context)
Error Analysis Before Model Change
# BAD
for model in models:
results = test(model)
# GOOD
failures = analyze_errors(results)
# Then decide if model change is warranted