Arize Instrumentation
Adds Arize AX tracing to an LLM application for the first time.
Install
npx skills add github/awesome-copilot --skill arize-instrumentation -g -a claude-code -y- Repository
- github/awesome-copilot40.7k
- Category
- AI and agents
- Updated
- 6d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
Arize Instrumentation Skill
Use this skill when the user wants to add Arize AX tracing to their application. Follow the two-phase, agent-assisted flow from the Agent-Assisted Tracing Setup and the Arize AX Tracing, Agent Setup Prompt.
Quick start (for the user)
If the user asks you to "set up tracing" or "instrument my app with Arize", you can start with:
Follow the instructions from https://arize.com/docs/PROMPT.md and ask me questions as needed.
Then execute the two phases below.
Core principles
- Prefer inspection over mutation, understand the codebase before changing it.
- Do not change business logic, tracing is purely additive.
- Use auto-instrumentation where available, add manual spans only for custom logic not covered by integrations.
- Follow existing code style and project conventions.
- Keep output concise and production-focused, do not generate extra documentation or summary files.
- NEVER embed literal credential values in generated code, always reference environment variables (e.g.,
os.environ["ARIZE_API_KEY"],process.env.ARIZE_API_KEY). This includes API keys, space IDs, and any other secrets. The user sets these in their own environment; the agent must never output raw secret values.
Phase 0: Environment preflight
Before changing code:
