Skills

Arize Link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling...

Install

npx skills add github/awesome-copilot --skill arize-link -g -a claude-code -y
Repository
github/awesome-copilot40.7k
Category
Design
Updated
6d ago
License
MIT
Context cost
Lightabout 1,018 tokens
Runs scripts
No, instructions only

What it does

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

Arize Link

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

When to Use

  • User wants a link to a 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 or context (exported trace data, parsed URLs):

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
queue_id, specific queue (omit for list)
evaluator_id [+ version], evaluator

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)

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
Read the whole SKILL.md on GitHub

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