Research: arize-instrumentation
Cached research evidence for arize-instrumentation (not authority).
Purpose
Section titled “Purpose”Arize skill for adding Arize AX / Phoenix tracing to LLM apps in two-phase agent-assisted flow: analyze codebase then implement instrumentation (LLM calls, tools, chains) across Python/TS/Java + 30+ integrations. Uses OpenInference/OpenTelemetry.
Harness Coverage
Section titled “Harness Coverage”Target agents: antigravity, claude-code, codex, crush, cursor, gemini-cli, github-copilot, grok, opencode.
Trust And Risks
Section titled “Trust And Risks”trust_tier=needs-inspection; status=inspect-then-install; provenance=verified-install-command; Arize official; requires ARIZE_API_KEY / SPACE_ID; two-phase edit flow; inspect for key handling and what spans are exported.
Install Prerequisites
Section titled “Install Prerequisites”Install: npx skills add Arize-ai/arize-skills --skill arize-instrumentation; export keys; status=inspect-then-install; selector=named; policy=Inspect source, hooks, scripts, credentials, and dedupe before install.
Upstream Maintainer
Section titled “Upstream Maintainer”Arize-ai/arize-skills (official Arize)
Comparable Alternatives
Section titled “Comparable Alternatives”Other tracing skills (phoenix-tracing, sentry-setup-ai-monitoring, langfuse, openinference).
> Evidence synthesized from public web sources (GitHub repos, official docs, skill registries); confidence reflects source reputation and public signals only. Not an endorsement.