SkillsAdd

github/awesome-copilot · Official

phoenix-evals

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

All-time #9286 First seen Apr 2, 2026
8-week activity · all time api

Installation

$ npx skills add github/awesome-copilot --skill phoenix-evals
View on skills.sh ↗ GitHub repository ↗

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

phoenix-evals Linked neighbor
arize-ai/phoenix

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

1.4K installs
frontend-design Same topic
anthropics/skills

Guidance for distinctive, intentional visual design when building new UI or reshaping an existi…

866.4K installs
agent-browser Same topic
vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, includ…

810.4K installs
vercel-react-best-practices Same topic
vercel-labs/agent-skills 697.8K installs
web-design-guidelines Same topic
vercel-labs/agent-skills

Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "chec…

617.3K installs
microsoft-foundry Same topic
microsoft/azure-skills

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and …

576.5K installs

Also in this package

Other skills from github/awesome-copilot · top by installs.

npx skills add github/awesome-copilot
git-commit 44.5K excalidraw-diagram-generator 29.1K documentation-writer 26.2K multi-stage-dockerfile 23.4K prd 22.9K gh-cli 21.9K refactor 21.6K java-springboot 19.9K create-readme 18.1K playwright-generate-test 18K

Browse all from github/awesome-copilot

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

Stars 38.8K
License LICENSE
Default branch main
Open issues 21
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseApache-2.0
CompatibilityRequires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai/phoenix-client.
Declared agents github-copilot
More metadata
version
1.0.0
languages
Python, TypeScript

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,611 B
  • docs SUMMARY.md 81 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,100 installs

SKILL.md

Phoenix Evals

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

Quick Reference

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

Workflows

Starting Fresh: [observe-tracing-setup](references/observe-tracing-setup.md) → [error-analysis](references/error-analysis.md) → [axial-coding](references/axial-coding.md) → [evaluators-overview](references/evaluators-overview.md)

Building Evaluator: [fundamentals](references/fundamentals.md) → [common-mistakes-python](references/common-mistakes-python.md) → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}

RAG Systems: [evaluators-rag](references/evaluators-rag.md) → evaluators-code- (retrieval) → evaluators-llm- (faithfulness)

Production: [production-overview](references/production-overview.md) → [production-guardrails](references/production-guardrails.md) → [production-continuous](references/production-continuous.md)

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