This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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
Understand experiments	experiments-overview
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

Copy & paste — that's it

npx skills add github/awesome-copilot --skill "phoenix-evals" --full-depth

Run this in your project — your agent picks the skill up automatically.

No common issues documented yet. If you hit a problem, the repository's GitHub Issues page is the best place to look.

Licensed under MIT— you can use, modify, and redistribute it under that license's terms.

View the full license file on GitHub →