Arize Trace Skill
Skill Verified ActiveDownloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
To enable users to inspect and debug their LLM applications by exporting and analyzing trace and span data from Arize.
Features
- Export Arize traces by ID
- Export Arize spans by ID
- Export Arize sessions by ID
- Investigate root causes using ax CLI
- Filter trace and span data
Use Cases
- Looking at existing trace data
- Seeing what an LLM app is doing
- Exporting traces for offline analysis
- Investigating runtime errors
- Analyzing behavior regressions
Non-Goals
- Modifying Arize data
- Real-time monitoring of live traces
- Configuring Arize itself
Workflow
- Identify the need to inspect Arize traces/spans.
- Determine the appropriate `ax` command (e.g., `spans export`, `traces export`) based on the data needed.
- Construct the command with necessary arguments (PROJECT, IDs, filters, time ranges).
- Execute the command, handling authentication and profile setup as needed.
- Analyze the exported JSON data for debugging or understanding application behavior.
Prerequisites
- Requires the ax CLI
- Requires a configured Arize profile
Installation
npx skills add github/awesome-copilotRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedSimilar Extensions
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LangSmith Observability
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Arize Prompt Optimization
100Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Arize Experiment
100Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
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100Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.