phoenix-evals
Build and run evaluators for AI/LLM applications using Phoenix.
npx skills add majiayu000/claude-skill-registry --skill phoenix-evals --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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` | | Build code evaluator | `evaluators-code-{python\|typescript}` | | Build LLM evaluator | `evaluators-llm-{python\|typescript}`, `evaluators-custom-templates` | | Run experiment | `experiments-running-{python\|typescript}` | | Create dataset | `experiments-datasets-{python\|typescript}` | | Validate evaluator | `validation`, `validation-calibration-{python\|typescript}` | | Analyze errors | `error-analysis`, `axial-coding` | | RAG evals | `evaluators-rag` | | Production | `production-overview`, `production-guardrails` | ## Workflows **Starting Fresh:** `observe-tracing-setup` → `error-analysis` → `axial-coding` → `evaluators-overview` **Building Evaluator:** `fundamentals` → `evaluators-{code\|llm}-{python\|typescript}` → `validation-calibration-{python\|typescript}` **RAG Systems:** `evaluators-rag` → `evaluators-code-*` (retrieval) → `evaluators-llm-*` (faithfulness) **Production:** `production-overview` → `production-guardrails` → `production-continuous` ## Rule Categorie
- Quick Reference
- Workflows
- Rule Categories
- Key Principles
What does the phoenix-evals skill do?
Build and run evaluators for AI/LLM applications using Phoenix.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill phoenix-evals --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.