Arize-Link
Arize-Link是一款design方向的AI技能,核心价值是Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs,可用于解决开发者在design领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
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.
mkdir -p ./skills/arize-link && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/arize-link/SKILL.md -o ./skills/arize-link/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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-configsTime 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 refere
🎯 Best For
- Developers scaffolding new projects
- Prototype builders
- UI designers
- Product designers
- Claude users
💡 Use Cases
- Bootstrapping React components
- Creating API route handlers
- Generating component mockups
- Creating design system tokens
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Arize-Link to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Can I customize the generated output?
Yes — modify the skill's prompt instructions to match your project conventions and coding style.
Does this work with Figma?
Some design skills integrate with Figma plugins. Check the Works With section for supported tools.
Does Arize-Link generate production-ready design specs?
It generates detailed specifications that developers can use directly. Review and adjust for your specific design system.
How do I install Arize-Link?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/arize-link/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Using generated code without understanding
Understand what generated code does before shipping it to production.
Skipping usability testing
AI-generated designs should be validated with real users before development.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.