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.
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`.
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
Annotate traces/spans with feedback scores via the SDK or the UI.
Step 4 — Evaluate with LLM-as-a-judge metrics
from opik.evaluation.metrics import Hallucination
metric = Hallucination()
score = metric.score(
input="What is the capital of France?",
output="Paris",
context=["France is a country in Europe."],
)
print(score)
Built-in judges include Hallucination, Moderation, Answer Relevance, Context
Precision/Recall; heuristic metrics and custom metrics are also supported.
Step 5 — Datasets, Experiments, and CI gates
Manage Datasets and run Experiments to compare prompt/model
variants during development
Wire evaluations into CI/CD with the PyTest integration so regressions
block merges
Iterate on prompts/models in the Prompt Playground
Step 6 — Production monitoring and optimization
Opik is built for scale: 40M+ traces/day ingestion
Track feedback scores, trace counts, and token usage in the Opik Dashboard
Add Online Evaluation Rules (LLM-as-a-judge on production traffic) to
catch issues live
Use Opik Agent Optimizer (dedicated SDK) to improve prompts/agents and
Opik Guardrails for safe-AI policies
This skill folder is plugin-installable through the standard oh-my-skills
flow so the wrapper, references, and installer script land on disk for any
supported agent runtime:
# Project install (writes into .agents/skills/opik/)
npx skills add https://github.com/akillness/oh-my-skills --skill opik
# Global install for every detected agent
npx skills add -g https://github.com/akillness/oh-my-skills --skill opik
# Target specific agents
npx skills add -g https://github.com/akillness/oh-my-skills --skill opik -a claude-code -a codex -y
The skill also ships scripts/install.sh as a
one-shot installer covering SDK install (uv → pip fallback) and optional
local self-hosting (OPIK_INSTALL_MODE=local).
Output format
When the user asks opik for help, return a compact brief:
# opik Routing Brief## Scope- Server mode: cloud | docker-local | kubernetes | undecided
- SDK: python | typescript | ruby-otel
- Lifecycle stage: tracing | evaluation | ci-gate | production-monitoring | optimization | guardrails
## Recommended next move- install-sdk | opik-configure | start-local-server | wire-integration | add-judge-metric | create-dataset-experiment | enable-online-rules
## Why- 2-3 bullets grounded in the user's packet
## Route-outs-`langsmith` when the observability stack is LangSmith
-`monitoring-observability` for non-LLM dashboards/alerts
-`data-analysis` for offline KPI/metric interpretation
Best practices
Start with cloud or ./opik.sh, not Kubernetes — Helm is for
production scale; local Docker Compose answers "does tracing work" in
minutes.
Prefer a native integration over hand-rolled @opik.track when the
framework is in the support table — integrations capture provider
metadata (tokens, model, latency) automatically.
Check the changelog before upgrading a self-hosted server — e.g.
v1.7.0 shipped breaking changes.
Judge metrics need context — Hallucination and RAG metrics score
against the context you pass; empty context produces misleading scores.
Gate CI on small, stable datasets — PyTest-integrated experiments
should be fast and deterministic; keep large sweeps in scheduled runs.
Turn production checks into Online Evaluation Rules instead of
re-running offline experiments against live traffic.
