github/awesome-copilot1k installs

phoenix-evals

Q: How do I install this agent skill?

Run npx skills add https://github.com/github/awesome-copilot --skill phoenix-evals.

Q: Is this agent skill safe to install?

Review the displayed partner audits, complete source, permissions, dependencies, and exact revision before installing.

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

How do I install this agent skill?

npx skills add https://github.com/github/awesome-copilot --skill phoenix-evals

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Is this agent skill safe to install?

Gen Agent Trust Hubpass
This skill provides comprehensive documentation and examples for building AI evaluators using Arize Phoenix. No malicious patterns were detected. It follows security best practices by recommending XML delimiters for LLM prompts and uses standard package management for its dependencies.
Socketpass
No alerts
Snykpass
Risk: LOW · No issues
ZeroLeakspass
Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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

How can the creator link this skill?

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/github/awesome-copilot/phoenix-evals">View phoenix-evals on skillZs</a>