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agentsop-observability-setup

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Choose a tracing backend and wire it fast

Software Engineering#observability#tracing#phoenix#mlflow#langsmith#llm-evals#autologging
Authoragentsope
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill solves the problem of not knowing which observability backend to choose for your LLM project, and failing to turn tracing on before your first production deploy.

Core Features & Use Cases

  • Backend decision + handoff: selects the best backend (LangSmith / Phoenix / MLflow / Langfuse / OpenTelemetry GenAI) based on stack, scale, hosting, and budget, then defers to the chosen backend’s own skill for depth.
  • One-line autolog wiring: installs tracing quickly via minimal configuration (env vars or framework auto-instrumentation) so traces start appearing on the next LM call.
  • Verify and make traces actionable: verifies that a trace actually lands in the UI, then adds eval hooks so traces become a regression-catching system.

Quick Start

Ask an AI to install and use the agentsop-observability-setup skill to wire a one-line tracing backend for your LLM project and verify traces appear before your first deploy.

Dependency Matrix

Required Modules

None required

Components

references

đź’» Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: agentsop-observability-setup
Download link: https://github.com/agentsope/SkillAlchemy/archive/main.zip#agentsop-observability-setup

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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