opik

name	opik
description	Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.
allowed-tools	Bash Read Write Edit Glob Grep WebFetch
compatibility	Cross-platform observability/eval wrapper usable from Claude Code, Codex, Gemini CLI, and OpenCode. Wraps the upstream Opik server (cloud, Docker, Kubernetes) and the Python/TypeScript SDKs. Routes LangSmith-stack work to `langsmith`, generic service dashboards/alerts to `monitoring-observability`, and offline KPI interpretation to `data-analysis`.
metadata	{"tags":"opik, comet, llm-observability, tracing, evaluation, llm-as-a-judge, prompt-optimization, guardrails, monitoring, plugin","platforms":"Claude, Gemini, Codex, OpenCode, All","keyword":"opik","version":"1.0.0","upstream":"https://github.com/comet-ml/opik","installer":"pip install opik && opik configure","source":"akillness/oh-my-skills"}

opik — Open-source LLM Observability, Evaluation & Optimization

Opik (built by Comet) streamlines the entire LLM application lifecycle: deep tracing of LLM calls and agent activity, LLM-as-a-judge evaluation, experiment management, production monitoring at scale (40M+ traces/day), plus the Opik Agent Optimizer and Opik Guardrails. This skill is the routing-first wrapper — it picks the right deployment mode, wires the SDK, and drives the trace → evaluate → monitor → optimize loop.

When to use this skill

• The user asks to install or configure Opik (pip install opik, opik configure, ./opik.sh)
• The user wants tracing for LLM calls or agents — via @opik.track or a framework integration (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Haystack, Ollama, Bedrock, Vercel AI SDK, Pydantic AI, …)
• The user wants LLM-as-a-judge metrics: hallucination detection, moderation, answer relevance, context precision/recall for RAG
• The user wants Datasets + Experiments evaluation, or PyTest-integrated LLM evaluation in CI/CD
• The user wants production LLM monitoring dashboards, online evaluation rules, prompt playground experiments, agent/prompt optimization, or guardrails

When not to use this skill

• The stack is LangSmith, not Opik → use langsmith
• The user needs generic service dashboards/alerts (non-LLM telemetry, uptime, infra metrics) → use monitoring-observability
• The user wants offline dataset/KPI interpretation rather than LLM tracing/eval plumbing → use data-analysis
• The user is doing root-cause log forensics on app/container logs → use log-analysis

Prerequisites

Requirement	Notes
Python 3.8+ (SDK)	pip install opik or uv pip install opik
Docker + Docker Compose	Only for local self-hosting via ./opik.sh
Kubernetes + Helm	Only for scalable self-hosted deployments
Comet.com account	Only for the zero-setup cloud option

Instructions

Step 1 — Choose the server mode