AI Engineering Placement Quiz
rohitg00/ai-engineering-from-scratch
Runs a 10-question quiz across five areas to place a learner in the AI Engineering from Scratch curriculum, so they skip what they already know.
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output.
Install Evaluators
$ npx skills add Arize-ai/phoenix --skill evaluators -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Arize-ai/phoenix evaluators --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .claude/skills/evaluators && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .claude/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluatorsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Arize-ai/phoenix --skill evaluators -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Arize-ai/phoenix evaluators --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .agents/skills/evaluators && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .agents/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Arize-ai/phoenix --skill evaluators -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Arize-ai/phoenix evaluators --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .cursor/skills/evaluators && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .cursor/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Arize-ai/phoenix.git --path src/phoenix/server/agents/prompts/skills/evaluators--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Arize-ai/phoenix --skill evaluators -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Arize-ai/phoenix evaluators --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .gemini/skills/evaluators && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .gemini/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Arize-ai/phoenix evaluatorsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Arize-ai/phoenix --skill evaluators -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .github/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .github/skills/evaluators && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .github/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Arize-ai/phoenix --skill evaluators -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Arize-ai/phoenix evaluators --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Arize-ai/phoenix.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src/phoenix/server/agents/prompts/skills/evaluators .opencode/skills/evaluators && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "evaluators" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/evaluators into .opencode/skills/evaluators/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evaluators", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
evaluators- GitHub stars
- 12k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 882 words
- Files
- 1
- Skills in repo
- 39
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output.
- Works in 7 steps: Derive the grading task from the stated… → Inventory before creating. Read the… → Decide the labels. Choose a small,… → …
- The user wants to create a new evaluator
- SKILL.md covers The Authoring Loop, Reference Provenance, Choosing The Judgment Structure and Matching The Field Topology, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Evaluators is an agent skill from Arize-ai/phoenix. Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output. Trigger when the user wants to create a new evaluator, improve an existing one's logic or rubric, choose labels, or decide what to measure on a dataset or experiment. Do NOT trigger on: (1) manual prompt drafting (use playground), (2) running or comparing experiments themselves (use experiments), (3) cross-trace failure diagnosis with no evaluator in scope (use phoenix-error-analysis).
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Education, covering LLM observability and Quizzes and assessments. The repository describes itself as: AI Observability & Evaluation.
When your agent uses it
- The user wants to create a new evaluator
- Improve an existing ones logic
- Decide what to measure on a dataset
- Manual prompt drafting (use playground)
Example prompts
- “/evaluators”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Derive the grading task from the stated purpose — a hypothesis and its evaluator are one design
- Inventory before creating. Read the dataset's existing evaluators and check input-shape
- Decide the labels. Choose a small, mutually exclusive, collectively exhaustive set — often
- Locate the signal in the run's fields — a top-level key, a chat-style messages array,
- Write the judgment: a function that reads the field and returns the label or score, or a judge
- Calibrate against several representative cases covering the named failure modes — one preview is
- Iterate until the representative cases label correctly and the tradeoff is acceptable.
What it can do on your machine
Read from SKILL.md and the folder at commit 3383f07. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Evaluators loads about 1.7k tokens when it runs. Until then it costs about 124 tokens; SKILL.md has 882 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 882 words (~1,720 tokens).
“A Phoenix evaluator scores a run: it reads some subset of the run's input, output, reference, and metadata and returns named annotations — a label, a score, or both. The two artifact kinds — a code evaluator (a Python or…”
- name
- evaluators
- summary
- Design or refine a code or LLM evaluator — labels, logic or rubric, the field it reads, and representative preview cases.
Files
Just SKILL.md in src/phoenix/server/agents/prompts/skills/evaluators of Arize-ai/phoenix.
Open the folder on GitHubat commit 3383f07
Compare with similar skills
Evaluators next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Evaluators this skillArize-ai/phoenix | 12k | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch | 65k | — | ~2k | Automated safety check: Pass | MIT | |
| AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch | 65k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Claude Certification Tutorrohitg00/ai-engineering-from-scratch | 65k | — | ~3k | Automated safety check: Pass | MIT | |
| Generate Verifiers Envadithya-s-k/FineEnvs | 421 | 1 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Laya Integrationwdobry/laya-playground | 182 | — | ~3.2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Evaluators
What does Evaluators do?
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output. Evaluators is an agent skill from Arize-ai/phoenix. Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output.
When should I use Evaluators?
Evaluators fits situations like: the user wants to create a new evaluator; improve an existing ones logic; decide what to measure on a dataset; manual prompt drafting (use playground).
How do I install Evaluators in Claude Code?
Run `npx skills add Arize-ai/phoenix --skill evaluators -a claude-code`. Or copy the skill folder (src/phoenix/server/agents/prompts/skills/evaluators in Arize-ai/phoenix) into .claude/skills/evaluators in your project. Claude Code loads it when a task matches its description.
How do I install Evaluators in Codex?
Run `npx skills add Arize-ai/phoenix --skill evaluators -a codex`. Or copy the skill folder (src/phoenix/server/agents/prompts/skills/evaluators in Arize-ai/phoenix) into .agents/skills/evaluators in your project. Codex loads it when a task matches its description.
Can I use Evaluators in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Arize-ai/phoenix --skill evaluators -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/evaluators, .gemini/skills/evaluators, .github/skills/evaluators and .opencode/skills/evaluators in your project.
What does Evaluators need to run?
SKILL.md names no scripts, command-line tools or credentials: Evaluators is instructions for the agent only. Our summary lists: Python 3.
Does Evaluators access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Evaluators safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Evaluators use?
Evaluators has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Evaluators use?
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Evaluators?
Skills that share tags, products or a category with Evaluators: AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 65k stars), AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 65k stars), Claude Certification Tutor (rohitg00/ai-engineering-from-scratch, 65k stars) and Generate Verifiers Env (adithya-s-k/FineEnvs, 421 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Evaluators?
Arize-ai (a GitHub organization) maintains it in Arize-ai/phoenix, which has 11,738 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 7, 2026.
Source: Arize-ai/phoenix on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.