agentsop-observability-setup

agentsop-observability-setup is an LLM evaluation & observability skill for AI agents, published by agentsope in the SkillAlchemy repository on GitHub. Skillspace rates agentsop-observability-setup D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).

Grade

D

Quality

68 / 100

Safety

Passed

GitHub stars

162

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

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