Arize Link logo

Arize Link

Organization
Arize-ai
arize-link

Generates deep links to the Arize UI for projects, traces, spans, sessions, datasets, experiments, labeling queues, evaluators, and annotation configs. Discovers organization and project IDs with the ax CLI and produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a project, trace, span, session, dataset, experiment, evaluator, or annotation config in the Arize UI.

Overview

PublisherArize-ai
Repositoryarize-skills
Skill namearize-link
Stars
69
Forks
15
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Arize-ai on GitHub. Read the source before you install it.

Installation

Install the Arize Link AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Arize-ai/arize-skills/tree/main/skills/arize-link
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Arize-ai/arize-skills.git /tmp/arize-skills
mkdir -p .claude/skills
cp -r /tmp/arize-skills/skills/arize-link .claude/skills/arize-link
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Arize Link in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Arize Link on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Arize Link is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Arize Link

Generate deep links to the Arize UI for projects, traces, spans, sessions, datasets, experiments, labeling queues, evaluators, and annotation configs.

When to Use

  • User wants a link to a project, trace, span, session, dataset, labeling queue, evaluator, or annotation config
  • You have IDs from exported data or logs and need to link back to the UI
  • User asks to "open" or "view" any of the above in Arize

Required Inputs

Collect from the user, context (exported trace data or parsed URLs), or the ax CLI:

Always requiredResource-specific
org_id (base64)project_id + trace_id [+ span_id] — trace/span
space_id (base64)project_id + session_id — session
dataset_id — dataset
dataset_id + experiment_id (one or more) — experiment or comparison
experiment_id — experiment with no dataset
queue_id — specific queue (omit for list)
evaluator_id [+ version] — evaluator

Discover organization and project IDs

Prefer the CLI over asking the user for IDs that it can provide:

bash
ax organizations list --output json

Use .organizations[].id for the organization ID. If more than one organization is returned, match the user-provided organization name or ask them to choose.

To discover a project ID, list projects in the relevant space:

bash
ax projects list --space "{space_name_or_id}" --limit 100 --output json

Use the matching project's id; its space_id supplies the space ID required in the URL. The command accepts a space name or ID; if no space is known, run ax projects list --limit 100 --output json and select the matching project.

To discover experiment IDs, list the experiments on the dataset; each has an id and a dataset_id:

bash
ax experiments list --dataset "{dataset_name_or_id}" --space "{space_name_or_id}" --output json

All path IDs must be base64-encoded (characters: A-Za-z0-9+/=). A raw numeric ID produces a valid-looking URL that 404s. If the user provides a number, ask them to copy the ID directly from their Arize browser URL (https://app.arize.com/organizations/{org_id}/spaces/{space_id}/…). If you have a raw internal ID (e.g. Organization:1:abC1), base64-encode it before inserting into the URL.

URL Templates

Base URL: https://app.arize.com (override for on-prem)

Project:

{base_url}/organizations/{org_id}/spaces/{space_id}/projects/{project_id}

Trace (add &selectedSpanId={span_id} to highlight a specific span):

{base_url}/organizations/{org_id}/spaces/{space_id}/projects/{project_id}?selectedTraceId={trace_id}&queryFilterA=&selectedTab=llmTracing&timeZoneA=America%2FLos_Angeles&startA={start_ms}&endA={end_ms}&envA=tracing&modelType=generative_llm

Session:

{base_url}/organizations/{org_id}/spaces/{space_id}/projects/{project_id}?selectedSessionId={session_id}&queryFilterA=&selectedTab=llmTracing&timeZoneA=America%2FLos_Angeles&startA={start_ms}&endA={end_ms}&envA=tracing&modelType=generative_llm

Dataset (selectedTab: examples or experiments):

{base_url}/organizations/{org_id}/spaces/{space_id}/datasets/{dataset_id}?selectedTab=examples

Experiment (repeat experimentId to compare experiments side by side):

{base_url}/organizations/{org_id}/spaces/{space_id}/datasets/{dataset_id}/experiments/compare?experimentId={experiment_id}
{base_url}/organizations/{org_id}/spaces/{space_id}/datasets/{dataset_id}/experiments/compare?experimentId={experiment_id_a}&experimentId={experiment_id_b}

Experiment with no dataset:

{base_url}/organizations/{org_id}/spaces/{space_id}/experiments/{experiment_id}

URL-encode experiment IDs (= → %3D, + → %2B, / → %2F).

Queue list / specific queue:

{base_url}/organizations/{org_id}/spaces/{space_id}/queues
{base_url}/organizations/{org_id}/spaces/{space_id}/queues/{queue_id}

Evaluator (omit ?version=… for latest):

{base_url}/organizations/{org_id}/spaces/{space_id}/evaluators/{evaluator_id}
{base_url}/organizations/{org_id}/spaces/{space_id}/evaluators/{evaluator_id}?version={version_url_encoded}

The version value must be URL-encoded (e.g., trailing = → %3D).

Annotation configs:

{base_url}/organizations/{org_id}/spaces/{space_id}/annotation-configs

Time Range

CRITICAL: startA and endA (epoch milliseconds) are required for trace/span/session links — omitting them defaults to the last 7 days and will show "no recent data" if the trace falls outside that window.

Priority order:

  1. User-provided URL — extract and reuse startA/endA directly.
  2. Span start_time — pad ±1 day (or ±1 hour for a tighter window).
  3. Fallback — last 90 days (now - 90d to now).

Prefer tight windows; 90-day windows load slowly.

Instructions

  1. Gather IDs from user, exported data, URL context, or the CLI. Use ax organizations list --output json to obtain org_id; use ax projects list to obtain project_id when needed.
  2. Verify all path IDs are base64-encoded.
  3. Determine startA/endA using the priority order above for trace, span, and session links.
  4. Substitute into the appropriate template and present as a clickable markdown link.

Troubleshooting

ProblemSolution
"No data" / empty viewTrace outside time window — widen startA/endA (±1h → ±1d → 90d).
404ID wrong or not base64. Re-check org_id with ax organizations list --output json, project_id with ax projects list, and space_id from the browser URL.
Span not highlightedspan_id may belong to a different trace. Verify against exported span data.
org_id unknownRun ax organizations list --output json and use .organizations[].id. If the CLI cannot access the organization, ask the user to copy it from their Arize browser URL.

Related Skills

  • arize-trace: Export spans to get trace_id, span_id, and start_time.

Examples

See references/EXAMPLES.md for a complete set of concrete URLs for every link type.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Arize Link AI skill do?

Generates deep links to the Arize UI for projects, traces, spans, sessions, datasets, experiments, labeling queues, evaluators, and annotation configs. Discovers organization and project IDs with the ax CLI and produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a project, trace, span, session, dataset, experiment, evaluator, or annotation config in the Arize UI.

Why use Arize Link on TypingMind?

Because you install it once and use it with any model. Arize Link is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Arize Link in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Arize-ai/arize-skills/tree/main/skills/arize-link. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Arize Link?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Arize Link?

As many as you like. As long as a model supports skills, you can use Arize Link with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Arize Link AI skill free?

Yes. It is published on GitHub by Arize-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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