arize-instrumentation

arize-instrumentation is an LLM evaluation & observability skill for AI agents, published by boshi-xixixi in the TraeSkill repository on GitHub. Skillspace rates arize-instrumentation C (72/100) and the safety scan flagged it for review; the rating is based on static analysis only.

INVOKE THIS SKILL when adding Arize AX tracing or observability to an app for the first time, or when the user wants to instrument their LLM app or get started with LLM observability. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.

Grade
C
Quality
72 / 100
Safety
Needs review
GitHub stars
247

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubboshi-xixixi/TraeSkill

Our review

Architecture
100 / 100
Maturity
Structured: ships references or scripts
Security scan
Medium-risk patterns
License
MIT
Adoption
Adopted by 1 other repositories, in 1 collections

What the scanner flagged

  • reads environment variables

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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