LLM Trace Review Interface
ai-evals-course/evals-skills
Builds a browser-based annotation page for reviewing LLM traces one at a time with pass/fail labels, notes and saved results, tailored to your data.
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
Install Datasets
$ npx skills add Arize-ai/phoenix --skill datasets -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Arize-ai/phoenix datasets --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/datasets .claude/skills/datasets && 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 "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .claude/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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/datasetsType 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 datasets -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Arize-ai/phoenix datasets --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/datasets .agents/skills/datasets && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .agents/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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 datasets -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Arize-ai/phoenix datasets --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/datasets .cursor/skills/datasets && 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 "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .cursor/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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/datasets--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 datasets -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Arize-ai/phoenix datasets --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/datasets .gemini/skills/datasets && 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 "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .gemini/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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 datasetsInstalls 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 datasets -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/datasets .github/skills/datasets && 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 "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .github/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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 datasets -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 datasets --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/datasets .opencode/skills/datasets && 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 "datasets" agent skill from https://github.com/Arize-ai/phoenix/tree/main/src/phoenix/server/agents/prompts/skills/datasets into .opencode/skills/datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets", 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
datasets- GitHub stars
- 12k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 983 words
- Files
- 1
- Skills in repo
- 39
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
- Works in 3 steps: Honest measurement (train / validation /… → Facets (category / difficulty / type).… → Quick iteration (small chunks). A small…
- Tasks that involve LLM observability
- SKILL.md covers What an output actually means, How datasets feed evaluators…, Matching a dataset to the… and Splits, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Datasets is an agent skill from Arize-ai/phoenix. Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments. Load this whenever a dataset is in view or the user asks what a dataset is, how splits work, what an output "means", or how datasets relate to experiments and evals. This skill governs the judgment; any tool descriptions govern the mechanics.
Its SKILL.md is about 1.6k 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 AI & LLM Engineering, covering LLM observability and LLM evaluation. The repository describes itself as: AI Observability & Evaluation.
When your agent uses it
- Tasks that involve LLM observability
- Tasks that involve LLM evaluation
Example prompts
- “/datasets”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Honest measurement (train / validation / test). The classic ML division. A held-out test split that you never tune against gives a…
- Facets (category / difficulty / type). Splits like single-hop vs. multi-hop, easy vs. hard, or by topic let you break an experiment down…
- Quick iteration (small chunks). A small split is useful for a fast pass in the playground before committing to a full regression run over…
What it can do on your machine
Read from SKILL.md and the folder at commit 856100b. 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
Datasets loads about 1.6k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 983 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 983 words (~1,624 tokens).
“A dataset is a table of examples. Each example (row) has an input, an optional output, and optional metadata. A dataset is the unit you evaluate a prompt or application against: you run something over every example and compare what…”
- name
- datasets
- summary
- Reason well about Phoenix datasets — examples, outputs, splits, labels — and how they feed evaluators and experiments.
Files
Just SKILL.md in src/phoenix/server/agents/prompts/skills/datasets of Arize-ai/phoenix.
Open the folder on GitHubat commit 856100b
Compare with similar skills
Datasets 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 |
|---|---|---|---|---|---|---|
| Datasets this skillArize-ai/phoenix | 12k | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| LLM Trace Review Interfaceai-evals-course/evals-skills | 1.5k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Phoenix LLM ObservabilityOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Phoenix CLIgithub/awesome-copilot | 40k | 2 repos | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Error Analysisyonatangross/orchestkit | 289 | — | ~3.6k | Automated safety check: Notes | MIT | |
| Failproof AI SDK IntegrationFailproofAI/failproofai | 5.3k | — | ~6k | Automated safety check: Pass | Custom licence |
Similar skills
Phoenix LLM Observability
Orchestra-Research/AI-Research-SKILLs
Sets up Arize Phoenix to trace, evaluate and monitor LLM applications, with instrumentation for OpenAI, LangChain and LlamaIndex and a self-hosted server.
- Official
Phoenix CLI
github/awesome-copilot
Debug LLM applications using the Phoenix CLI. An agent skill from github/awesome-copilot.
Error Analysis
yonatangross/orchestkit
Evals-first error analysis for LLM apps: clusters real Langfuse or JSONL traces into a human-confirmed failure taxonomy with counts, then recommends binary pass/fail evals for recurring named modes.
Failproof AI SDK Integration
FailproofAI/failproofai
Helps instrument a custom Python or TypeScript agent to record events for Failproof AI, verify what gets written, and run an evaluator worker that scores the runs.
- Official
Phoenix Evals
github/awesome-copilot
Build and run evaluators for AI/LLM applications using Phoenix.
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Related
Categories
Questions about Datasets
What does Datasets do?
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments. Datasets is an agent skill from Arize-ai/phoenix. Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments.
When should I use Datasets?
Datasets fits situations like: tasks that involve LLM observability; tasks that involve LLM evaluation.
How do I install Datasets in Claude Code?
Run `npx skills add Arize-ai/phoenix --skill datasets -a claude-code`. Or copy the skill folder (src/phoenix/server/agents/prompts/skills/datasets in Arize-ai/phoenix) into .claude/skills/datasets in your project. Claude Code loads it when a task matches its description.
How do I install Datasets in Codex?
Run `npx skills add Arize-ai/phoenix --skill datasets -a codex`. Or copy the skill folder (src/phoenix/server/agents/prompts/skills/datasets in Arize-ai/phoenix) into .agents/skills/datasets in your project. Codex loads it when a task matches its description.
Can I use Datasets 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 datasets -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datasets, .gemini/skills/datasets, .github/skills/datasets and .opencode/skills/datasets in your project.
What does Datasets need to run?
SKILL.md names no scripts, command-line tools or credentials: Datasets is instructions for the agent only.
Does Datasets 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 Datasets 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 Datasets use?
Datasets 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 Datasets use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Datasets?
Skills that share tags, products or a category with Datasets: LLM Trace Review Interface (ai-evals-course/evals-skills, 1.5k stars), Phoenix LLM Observability (Orchestra-Research/AI-Research-SKILLs, 13k stars), Phoenix CLI (github/awesome-copilot, 40k stars) and Error Analysis (yonatangross/orchestkit, 289 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Datasets?
Arize-ai (a GitHub organization) maintains it in Arize-ai/phoenix, which has 11,744 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 8, 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.