Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Arize-ai/arize-skills --skill arize-datasetgit clone --depth 1 https://github.com/Arize-ai/arize-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arize-ai/arize-skills/arize-dataset)<a href="https://agentmods.dev/skills/arize-ai/arize-skills/arize-dataset"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-dataset/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arize-ai/arize-skills/arize-dataset"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-dataset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.03631 |
| Opus 5.5 | $0.00029 | $0.01452 |
| Sonnet 5.5 | $0.00015 | $0.00726 |
| Haiku 4.5 | $0.00007 | $0.00363 |
Grade A, and why
arize-dataset scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
83% identical to arize-dataset — 149 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Dataset Skill
SPACE—--spaceflags accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
Concepts
- Dataset = a versioned collection of examples used for evaluation and experimentation
- Dataset Version = a snapshot of a dataset at a point in time; updates can be in-place or create a new version
- Example = a single record in a dataset with arbitrary user-defined fields (e.g.,
question,answer,context) - Space = an organizational container; datasets belong to a space
System-managed fields on examples (id, created_at, updated_at) are auto-generated by the server -- never include them in create or append payloads.
Prerequisites
Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront.
If an ax command fails, troubleshoot based on the error:
command not foundor version error → see references/ax-setup.md401 Unauthorized/ missing API key → runax profiles showto inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys- Space unknown → see Space
- Project unclear → ask the user, or run
ax projects list -o json --limit 100and present as selectable options - Security: Never read
.envfiles or search the filesystem for credentials. Exception: the non-secretARIZE_SPACE_IDline (see Space). Useax profilesfor Arize credentials andax ai-integrationsfor LLM provider keys. Never ask the user to paste secrets into chat. For missing credentials, see references/ax-profiles.md.
List Datasets: ax datasets list
Browse datasets in a space. Listing is for human browsing; when the dataset name or ID is known, call ax datasets get NAME_OR_ID --space SPACE -o json directly.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago Changed · -67 lines 0ec2186d3b35
- 25d ago Changed 96e3b663e56a
- 1mo ago Changed · +1 lines dc2292966f39
- 1mo ago First seen · 410 lines · 73 tokens per session scan A 6bbded91fd53
arize-dataset is a skill published in the GitHub repository Arize-ai/arize-skills (66 stars, last pushed 4d ago), licensed MIT. It adds 73 tokens to every session and 3,631 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to arize-dataset, differing in 149 lines, and is treated as a copy.
Other skills, from other repositories
trulens-evaluation-setup
Configure feedback functions and selectors for TruLens evaluations.
trulens-instrumentation
Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
trulens-blocking-guardrails
Configure and use feedback functions as runtime blocking guardrails.
trulens-dataset-curation
Create and curate evaluation datasets with ground truth for TruLens.
edd
Eval-driven development loop for this repo (rag-loop, an aircraft-systems RAG app on LangGraph, OpenAI, Qdrant Cloud, and TruLens). Use when the user wants to run an experiment, inspect feedback metrics, or decide keep-or-reject on a config/prompt change.