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 build-with-dhiraj/ai-workflow-framework-portability-kit --skill arize-experimentgit clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitWrote 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/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-experiment)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-experiment"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-experiment/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/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-experiment"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.04809 |
| Opus 5.5 | $0.00029 | $0.01924 |
| Sonnet 5.5 | $0.00015 | $0.00962 |
| Haiku 4.5 | $0.00007 | $0.00481 |
Grade A, and why
arize-experiment 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 25d 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
86% identical to arize-experiment — 31 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 — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Experiment Skill
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
Concepts
- Experiment = a named evaluation run against a specific dataset version, containing one run per example
- Experiment Run = the result of processing one dataset example -- includes the model output, optional evaluations, and optional metadata
- Dataset = a versioned collection of examples; every experiment is tied to a dataset and a specific dataset version
- Evaluation = a named metric attached to a run (e.g.,
correctness,relevance), with optional label, score, and explanation
The typical flow: export a dataset → process each example → collect outputs and evaluations → create an experiment with the runs.
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 → run
ax spaces listto pick by name, or ask the user - 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. Useax profilesfor Arize credentials andax ai-integrationsfor LLM provider keys. If credentials are not available through these channels, ask the user. - CRITICAL — Never fabricate outputs: When running an experiment, you MUST call the real model API specified by the user for every dataset example. Never fabricate, simulate, or hardcode model outputs, latencies, or evaluation scores. If you cannot call the API (missing SDK, missing credentials, network error), stop and tell the user what is needed before proceeding.
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.
- 25d ago First seen · 430 lines · 73 tokens per session scan A fa306d9f27ef
arize-experiment is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 12d ago), licensed MIT. It adds 73 tokens to every session and 4,809 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to arize-experiment, differing in 31 lines, and is treated as a copy.
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