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-prompt-optimizationgit 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-prompt-optimization)<a href="https://agentmods.dev/skills/arize-ai/arize-skills/arize-prompt-optimization"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-prompt-optimization/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-prompt-optimization"><img src="https://agentmods.dev/badge/skills/arize-ai/arize-skills/arize-prompt-optimization.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 | $0.00081 | $0.04289 |
| Opus 5 | $0.00041 | $0.02145 |
| Sonnet 5 | $0.00016 | $0.00858 |
| Haiku 4.5 | $0.00008 | $0.00429 |
Grade A, and why
arize-prompt-optimization 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 5d 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-prompt-optimization — 110 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 — 398 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Prompt Optimization Skill
SPACE—--spaceflags accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
Related skills
- arize-prompts: Create, version, and label prompts in Prompt Hub with
ax prompts(JSON messages, providers, labels such asproduction). Use that skill when the artifact should live in Arize; use arize-prompt-optimization below to improve prompt text from traces, datasets, and experiments.
Concepts
Where Prompts Live in Trace Data
LLM applications emit spans following OpenInference semantic conventions. Prompts are stored in different span attributes depending on the span kind and instrumentation:
| Column | What it contains | When to use |
|---|---|---|
attributes.llm.input_messages |
Structured chat messages (system, user, assistant, tool) in role-based format | Primary source for chat-based LLM prompts |
attributes.llm.input_messages.roles |
Array of roles: system, user, assistant, tool |
Extract individual message roles |
attributes.llm.input_messages.contents |
Array of message content strings | Extract message text |
attributes.input.value |
Serialized prompt or user question (generic, all span kinds) | Fallback when structured messages are not available |
attributes.llm.prompt_template.template |
Template with {variable} placeholders (e.g., "Answer {question} using {context}") |
When the app uses prompt templates |
attributes.llm.prompt_template.variables |
Template variable values (JSON object) | See what values were substituted into the template |
attributes.output.value |
Model response text | See what the LLM produced |
attributes.llm.output_messages |
Structured model output (including tool calls) | Inspect tool-calling responses |
Finding Prompts by Span Kind
- LLM span (
attributes.openinference.span.kind = 'LLM'): Checkattributes.llm.input_messagesfor structured chat messages, ORattributes.input.valuefor a serialized prompt. Checkattributes.llm.prompt_template.templatefor the template. - Chain/Agent span:
attributes.input.valuecontains the user's question. The actual LLM prompt lives on child LLM spans -- navigate down the trace tree. - Tool span:
attributes.input.valuehas tool input,attributes.output.valuehas tool result. Not typically where prompts live.
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.
- 5d ago Changed · -72 lines d0e1d465473c
- 9d ago First seen · 470 lines · 81 tokens per session scan A 0bc787c68f00
arize-prompt-optimization is a skill published in the GitHub repository Arize-ai/arize-skills (48 stars, last pushed yesterday), licensed MIT. It adds 81 tokens to every session and 4,289 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to arize-prompt-optimization, differing in 110 lines, and is treated as a copy.
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…