Arize Prompts

Arize Prompts Skill

`SPACE` — --space flags accept a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax 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

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_STRING for this style. Do not ask the user which variable format to use — default to F_STRING unless the template clearly uses Mustache {{...}} (use MUSTACHE) or you need NONE for literal braces with no substitution.
• Provider and model — the vendor and model this version targets. --provider is required by the CLI on every create and create-version. --model must 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 production and staging are 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-message in the CLI.

Playground traces: Every prompt you test in the Playground is automatically logged to the Playground Traces project as a trace, making test runs available for analysis, debugging, and evaluation — no extra instrumentation needed.

The tutorial at https://arize.com/docs/ax/prompts/tutorial/create-a-prompt walks through authoring in the UI. This skill covers the CLI side of the same objects.

Prerequisites

Proceed directly — run the ax subcommand you need. Do NOT check versions, env vars, or profiles upfront.

If a command fails:

• command not found or version errors → references/ax-setup.md
• 401 / profile issues → ax profiles show, then references/ax-profiles.md; API keys: https://app.arize.com/admin
• Space unknown → ax spaces list
• LLM calls from Hub/Playground need provider credentials → arize-ai-provider-integration (ax ai-integrations list --space SPACE)
• Security: Never read .env or search the filesystem for secrets. Use ax profiles and ax ai-integrations only. Never ask the user to paste secrets into chat. For missing credentials, see references/ax-profiles.md.

When you must ask the user first

Prefer resolving gaps with ax (e.g. ax spaces list, ax prompts list, ax prompts get) instead of pausing. If something is still ambiguous or unsafe without confirmation, use this framing:

1. I found the arize-prompts skill in this repo
2. A few clarifying questions before I invoke it:
3. Ask minimal numbered questions — only what blocks the next ax prompts command.

Do not ask about `--input-variable-format` — always default to F_STRING for {variable} templates.

Eliciting the prompt template

Hub prompts are templates: the stored strings matter. When the user asks to create or save a prompt but has not provided the exact system/user strings, your first move is elicitation — not a finished generic prompt. That is Workflow A (build before ax prompts create).

1. Ask for the prompt template — the actual wording they want in each role: "Paste or type the prompt template (the exact system and user text you want saved)."
2. In the same turn, state the variable convention: You must reference each variable in single curly braces — { + name + } (e.g. {question}, {context}), not bare names and not {{name}} unless they explicitly need Mustache.
3. Assemble the JSON messages array from their template lines per role.

Anti-patterns — avoid these:

• Inventing a stock generic messages array (e.g. {task} / {context} / {constraints}) when the user just said "create a prompt" — this writes Hub content for them and skips elicitation
• Asking "What should this prompt do?" instead of asking for the literal template
• Process narration like "checking the prompts skill and your open file…" — go straight to elicitation
• Omitting --provider or --model from any proposed command
• Deferring to another agent (e.g. pointing users at a “Playground Agent” for extra patterns) instead of finishing with Workflows A–D — stay within this skill and the official doc links at the top

Optional starter: Only if the user explicitly asks for a draft or example, offer a short labeled starter they can replace — still elicit their real template afterward.

Messages file format

--messages must be a non-empty JSON array. Each object needs role; commonly also content. Optional: tool_call_id, tool_calls.

Format-only example (not a default to paste — see Eliciting the prompt template):

[
  {"role": "SYSTEM", "content": "You are a concise trip planner. Keep responses under 200 words."},
  {"role": "USER", "content": "{duration} itinerary for {destination} ({travel_style} style):\nResearch: {research}\nBudget: {budget_info}"}
]

Providers (--provider): OPEN_AI, ANTHROPIC, AZURE_OPEN_AI, AWS_BEDROCK, VERTEX_AI, CUSTOM. (No GEMINI option for prompts, unlike ax ai-integrations.) Required on every create and create-version.

Model (--model): Always pass an explicit model. If unknown, propose a provider-appropriate default and confirm before running.

Variable format: Placeholders must use single braces {name}. Always pass --input-variable-format F_STRING for that shape. Only use MUSTACHE for {{name}} or NONE for no interpolation — do not ask the user unless they stated a non-default requirement.

Recommended order