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-promptsgit 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-prompts)<a href="https://agentmods.dev/skills/arize-ai/arize-skills/arize-prompts"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-prompts/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-prompts"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-prompts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 280 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- medium Excessive Agency · line 38 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 59 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 63 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 109 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00110 | $0.05074 |
| Opus 5 | $0.00055 | $0.02537 |
| Sonnet 5 | $0.00022 | $0.01015 |
| Haiku 4.5 | $0.00011 | $0.00507 |
Grade A, and why
arize-prompts 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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- arize-prompts — 91% identical, 64 lines differ
How it starts
The opening of the file, as written. The whole thing — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Prompts Skill
SPACE—--spaceflags accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
Official references (read the skill body first; open docs only if the user needs UI walkthroughs):
- CLI: https://arize.com/docs/api-clients/cli/prompts
- Creating prompts in the product (Prompt Playground, variables, params): https://arize.com/docs/ax/prompts/tutorial/create-a-prompt
See references/cli-prompts.md for full flag tables.
How this skill fits into the prompt workflow
| Skill | Use it for |
|---|---|
This skill (arize-prompts) |
Workflows A–B: build or import templates and save · C: labels / promote · D: list, get, edit description, new version for message changes, delete, duplicate |
| arize-prompt-optimization | Improving prompt text using traces, datasets, experiments, and the optimization meta-prompt — often after you know what to change |
| arize-experiment | Running dataset experiments that consume Hub prompts or column-mapped inputs |
| arize-evaluator | Scoring prompt outputs with LLM-as-judge |
Typical loop: Author or elicit the prompt (Playground or chat) → save to Hub → run experiments (arize-experiment) → evaluate outputs (arize-evaluator) → optimize (arize-prompt-optimization) → save new version → promote with labels.
Concepts: what is a prompt in Arize?
A prompt in Prompt Hub is a named, versioned template stored in a space — not a one-off string in code. It is an artifact you can open in the Playground, diff across versions, and wire to experiments or production workflows.
Each prompt includes:
- Messages — an ordered chat transcript (system, user, assistant, tool roles) as stored JSON. Typically a system message for behavior and a user message as the template that receives dataset or runtime variables.
- Template variables — must be written with single curly braces around each name:
{+ identifier +}(same shape as{}with the variable name inside), e.g.{question},{context}. Filled at runtime by experiments or your app. Always use--input-variable-format F_STRINGfor this style. Do not ask the user which variable format to use — default toF_STRINGunless the template clearly uses Mustache{{...}}(useMUSTACHE) or you needNONEfor literal braces with no substitution. - Provider and model — the vendor and model this version targets.
--provideris required by the CLI on everycreateandcreate-version.--modelmust always appear in commands this skill proposes — pick an explicit model string, propose a sensible default if unknown, and confirm before running. - Invocation parameters — optional model settings like temperature and max tokens, configured under Params in the UI. CLI flows still require provider and explicit model alongside messages and format.
- Version history — every material change creates a new immutable version. Labels like
productionandstagingare mutable pointers to specific versions so your app code never needs to change when you promote a new version. - Version description — the optional text on Save New Version in the Hub UI is the same concept as
--commit-messagein the CLI.
What ships with it
3 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.
- yesterday Changed 4572b98303f5
- 13d ago First seen · 404 lines · 110 tokens per session scan A 6d626d375e88
arize-prompts is a skill published in the GitHub repository Arize-ai/arize-skills (50 stars, last pushed 2d ago), licensed MIT. It adds 110 tokens to every session and 5,074 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
playground
Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground ui. operation call, including single-shot prompt rewrites.
experiments
Run, read, and compare dataset-backed experiments to find evidence that a prompt or pipeline is improving. Trigger when the user wants to iterate over a dataset with experiments, compare experiment runs, read experiment quality/latency/cost, or decide whether a change actually helped. Running a prompt over a dataset…
optimize-prompts
Use this to improve a prompt systematically instead of hand-tweaking it by feel. Trigger on "optimize my prompt", "make this prompt better", "the prompt isn't working well", "auto-tune my prompt", "few-shot example selection", or when prompt quality has plateaued. Optimize against an eval set with a method, and let…
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…