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From phoenix-evals
Build and run evaluators for AI/LLM applications using Phoenix. Covers code-first eval design, judge model selection, RAG evals, synthetic data generation, and production guardrails.
How this skill is triggered — by the user, by Claude, or both
Slash command
/phoenix-evals:phoenix-evalsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.
references/axial-coding.mdreferences/common-mistakes-python.mdreferences/error-analysis-multi-turn.mdreferences/error-analysis.mdreferences/evaluate-dataframe-python.mdreferences/evaluators-code-python.mdreferences/evaluators-code-typescript.mdreferences/evaluators-custom-templates.mdreferences/evaluators-llm-python.mdreferences/evaluators-llm-typescript.mdreferences/evaluators-overview.mdreferences/evaluators-pre-built.mdreferences/evaluators-rag.mdreferences/experiments-datasets-python.mdreferences/experiments-datasets-typescript.mdreferences/experiments-overview.mdreferences/experiments-running-python.mdreferences/experiments-running-typescript.mdreferences/experiments-synthetic-python.mdreferences/experiments-synthetic-typescript.mdBuild evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.
Starting Fresh: observe-tracing-setup → error-analysis → axial-coding → evaluators-overview
Building Evaluator: fundamentals → common-mistakes-python → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}
RAG Systems: evaluators-rag → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)
Production: production-overview → production-guardrails → production-continuous
| Prefix | Description |
|---|---|
fundamentals-* | Types, scores, anti-patterns |
observe-* | Tracing, sampling |
error-analysis-* | Finding failures |
axial-coding-* | Categorizing failures |
evaluators-* | Code, LLM, RAG evaluators |
experiments-* | Datasets, running experiments |
validation-* | Validating evaluator accuracy against human labels |
production-* | CI/CD, monitoring |
| Principle | Action |
|---|---|
| Error analysis first | Can't automate what you haven't observed |
| Custom > generic | Build from your failures |
| Code first | Deterministic before LLM |
| Validate judges | >80% TPR/TNR |
| Binary > Likert | Pass/fail, not 1-5 |
npx claudepluginhub simplycubed/skills --plugin phoenix-evals3plugins reuse this skill
First indexed Jun 6, 2026
Build and run evaluators for AI/LLM applications using Phoenix. Covers code-first eval design, judge model selection, RAG evals, synthetic data generation, and production guardrails.
Builds LangSmith evaluation pipelines: create LLM-as-Judge/custom evaluators, capture agent outputs/trajectories via run functions, run locally with evaluate() or CLI.
Evaluates and improves GenAI agent output quality using MLflow's native APIs for datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components.