Phoenix evals

Skill github/awesome-copilot/skills/phoenix-evals

Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.

Install

npx -y skills add github/awesome-copilot --skill phoenix-evals

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What its author says it does

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Build and run evaluators for AI/LLM applications using Phoenix.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Phoenix Evals

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

Quick Reference

Task	Files
Setup	setup-python, setup-typescript
Decide what to evaluate	evaluators-overview
Choose a judge model	fundamentals-model-selection
Use pre-built evaluators	evaluators-pre-built
Build code evaluator	evaluators-code-python, evaluators-code-typescript
Build LLM evaluator	evaluators-llm-python, evaluators-llm-typescript, evaluators-custom-templates
Batch evaluate DataFrame	evaluate-dataframe-python
Run experiment	experiments-running-python, experiments-running-typescript
Create dataset	experiments-datasets-python, experiments-datasets-typescript
Generate synthetic data	experiments-synthetic-python, experiments-synthetic-typescript
Validate evaluator accuracy	validation, validation-evaluators-python, validation-evaluators-typescript
Sample traces for review	observe-sampling-python, observe-sampling-typescript
Analyze errors	error-analysis, error-analysis-multi-turn, axial-coding
RAG evals	evaluators-rag
Avoid common mistakes	common-mistakes-python, fundamentals-anti-patterns
Production	production-overview, production-guardrails, production-continuous

Workflows

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

Reference Categories

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

Key Principles

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