arize-link
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Best use case
arize-link is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Teams using arize-link should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/arize-link/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How arize-link Compares
| Feature / Agent | arize-link | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 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.
## 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
```
**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
```
## 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, or URL context.
2. Verify all path IDs are base64-encoded.
3. Determine `startA`/`endA` using the priority order above.
4. Substitute into the appropriate template and present as a clickable markdown link.
## Troubleshooting
| 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}/…`. |
## 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.Related Skills
arize-trace
INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.
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INVOKE THIS SKILL when creating, managing, or using annotation configs on Arize (categorical, continuous, freeform), or applying human annotations to project spans via the Python SDK. Configs are the label schema for human feedback on spans and other surfaces in the Arize UI. Triggers: annotation config, label schema, human feedback schema, bulk annotate spans, update_annotations.
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INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.
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