From arize-skills
Generates deep URLs to Arize UI for traces, spans, sessions, datasets, evaluators, and annotation configs. Use when you have IDs and need to open or share resources in Arize.
How this skill is triggered — by the user, by Claude, or both
Slash command
/arize-skills:arize-linkThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs.
Generate deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs.
Collect from the user or context (exported trace data, parsed URLs):
| Always required | Resource-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.
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
Dataset (selectedTab: examples or experiments):
{base_url}/organizations/{org_id}/spaces/{space_id}/datasets/{dataset_id}?selectedTab=examples
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
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:
startA/endA directly.start_time — pad ±1 day (or ±1 hour for a tighter window).now - 90d to now).Prefer tight windows; 90-day windows load slowly.
startA/endA using the priority order above.| Problem | Solution |
|---|---|
| "No data" / empty view | Trace outside time window — widen startA/endA (±1h → ±1d → 90d). |
| 404 | ID wrong or not base64. Re-check org_id, space_id, project_id from the browser URL. |
| Span not highlighted | span_id may belong to a different trace. Verify against exported span data. |
org_id unknown | ax CLI doesn't expose it. Ask user to copy from https://app.arize.com/organizations/{org_id}/spaces/{space_id}/…. |
trace_id, span_id, and start_time.See references/EXAMPLES.md for a complete set of concrete URLs for every link type.
npx claudepluginhub arize-ai/arize-skills --plugin arize-skillsDownloads, exports, and inspects Arize traces and spans via the ax CLI to debug LLM app runtime issues, analyze errors, or review behavior regressions.
Retrieves and debugs trace and span data from Arize ML observability platform using arize_toolkit CLI. Lists recent traces, fetches by ID, shows spans, analyzes latency/tokens/cost, exports data.
Manages Arize AI datasets via ax CLI: list with pagination, get details, create from CSV/JSON/Parquet files, delete, export data, extract IDs with jq. For Arize ML platform ops.