opik-optimizer

Opik Optimizer

Purpose

Design, run, and interpret Opik Optimizer workflows for prompts, tools, and model parameters with consistent dataset/metric wiring and reproducible evaluation.

When to use

Use this skill when a user asks for:

• Choosing and configuring Opik Optimizer algorithms for prompt/agent optimization.
• Writing ChatPrompt-based optimization runs and custom metric functions.
• Optimizing with tools (function calling or MCP), selected prompt roles, or prompt segments.
• Tuning LLM call parameters with optimize_parameter.
• Comparing optimizer outputs and interpreting OptimizationResult.

Workflow

1. Select optimizer strategy (MetaPromptOptimizer, FewShotBayesianOptimizer, HRPO, etc.) based on the target optimization goal.
2. Build prompt/dataset/metric wiring and validate placeholder-field alignment.
3. Run prompt, tool, or parameter optimization with explicit controls (n_threads, n_samples, max_trials, seed).
4. Inspect OptimizationResult and compare score deltas against initial baselines.
5. Summarize recommendations, risks, and next experiments.

Inputs

• Target optimization objective (prompt/tool/parameter) and success metric.
• Dataset source and expected schema fields.
• Model/provider constraints and runtime limits.
• Optional scope constraints (optimize_prompts segments, tool fields, project names).

Outputs

• Optimizer run configuration and rationale.
• Result interpretation (score, initial_score, history trends).
• Recommended next changes and follow-up experiment plan.

Use the reference files in this skill for details before implementing code:

• references/algorithms.md
• references/prompt_agent_workflow.md
• references/example_patterns.md

Opik Optimizer quickstart

1. Install and import:

pip install opik-optimizer

from opik_optimizer import ChatPrompt, MetaPromptOptimizer, HRPO, FewShotBayesianOptimizer
from opik_optimizer import datasets

1. Build a prompt and metric: