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-prompt-optimizationgit 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-prompt-optimization)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-prompt-optimization"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/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/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-prompt-optimization"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/arize-prompt-optimization.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 | $0.00081 | $0.04742 |
| Opus 5.5 | $0.00032 | $0.01897 |
| Sonnet 5.5 | $0.00016 | $0.00948 |
| Haiku 4.5 | $0.00008 | $0.00474 |
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 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
84% identical to arize-prompt-optimization — 18 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 — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Prompt Optimization 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.
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
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 · 470 lines · 81 tokens per session scan A 80b753dd9777
arize-prompt-optimization 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 81 tokens to every session and 4,742 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to arize-prompt-optimization, differing in 18 lines, and is treated as a copy.
Other skills, from other repositories
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
guidance
Constrain LLM output with grammars; guarantee valid JSON.
omni-compression
Configure RTK (command output), Caveman (prose), and stacked compression modes. Manage language packs, custom rules, and test prompt compression reducing tokens by 60–90%.
cli-compression
Configure and test prompt compression from the CLI. Manage RTK filters, Caveman rules, stacked compression modes, and preview compression output with real prompts.
claude-api-in-prototypes
Call Claude from your HTML artifacts via window.claude.complete.
prompt-library
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.