Arize Instrumentation
Skill AktivAdds 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.
To enable users to easily add LLM observability and tracing to their applications using Arize AX by following a guided, agent-assisted workflow.
Funktionen
- Adds Arize AX tracing to LLM applications.
- Two-phase agent-assisted analysis and implementation flow.
- Supports Python, TypeScript/JavaScript, Java, and Go.
- Integrates with LLM providers, frameworks, and OpenTelemetry.
- Provides detailed guidance on manual span creation for tool use.
Anwendungsfälle
- When a user wants to instrument their application for the first time with Arize AX tracing.
- When a user needs to set up LLM observability from scratch.
- When a user wants to integrate OpenTelemetry or openinference with their LLM application.
- When a user needs to get started with Arize tracing and requires guided setup.
Nicht-Ziele
- Replacing core business logic of the application.
- Generating extensive documentation or summary files beyond the necessary code.
- Embedding literal credential values in generated code.
- Proactively checking `ax` installation or version before it's needed.
Workflow
- Phase 0: Environment preflight (confirm scope, identify runtime needs)
- Phase 1: Analysis (scan manifests, check imports, detect existing tracing, identify scope)
- Phase 1 Output: Summarize findings and propose integration
- Phase 2: Implementation (fetch docs, install packages, set credentials, centralize instrumentation, add manual spans if needed)
- Verification (app builds, starts, produces traces; check in Arize or provide blockers)
Praktiken
- Code Analysis
- Observability
- Instrumentation
- Agent-Assisted Development
Voraussetzungen
- Access to the codebase
- Python, TypeScript/JavaScript, Java, or Go runtime environment
- Arize API Key and Space ID (to be configured by the user)
Execution
- warning:Pinned dependenciesWhile installation instructions specify packages to install, they do not explicitly mandate pinning versions or provide lockfile generation steps, which could lead to unexpected behavior with dependency updates.
Installation
npx skills add github/awesome-copilotFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
Vertrauenssignale
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