/opik-optimizer

name: opik-optimizer
description: Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
license: MIT
metadata:
  source: "https://github.com/vincentkoc/dotskills"

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

2. Build a prompt and metric:

from opik.evaluation.metrics import LevenshteinRatio

prompt = ChatPrompt(
    system="You are a concise answerer.",
    user="{question}",
)

def metric(dataset_item: dict, output: str) -> float:
    return LevenshteinRatio().score(
        reference=dataset_item["answer"],
        output=output,
    ).value

3. Load dataset and run:

dataset = datasets.hotpot(count=30)

result = MetaPromptOptimizer(model="openai/gpt-5-nano").optimize_prompt(
    prompt=prompt,
    dataset=dataset,
    metric=metric,
    n_samples=20,
    max_trials=10,
)
result.display()

Core workflow you should follow

1. Pick optimizer class:

• Few-shot examples + Bayesian selection: `FewShotBayesianOptimizer`
• LLM meta-reasoning: `MetaPromptOptimizer`
• Genetic + MOO / LLM crossover: `EvolutionaryOptimizer`
• Hierarchical reflective diagnostics: `HierarchicalReflectiveOptimizer` (`HRPO`)
• Pareto-based genetic strategy: `GepaOptimizer`
• Parameter tuning only: `ParameterOptimizer`

2. Define a single `ChatPrompt` (or dict of prompts for multi-prompt cases). 3. Provide a dataset from `opik_optimizer.datasets`. 4. Provide metric callable with signature `(dataset_item, llm_output) -> float` (or `ScoreResult`/list of `ScoreResult`). 5. Set optimizer controls (`n_threads`, `n_samples`, `max_trials`, seed, etc.). 6. Run one of:

• `optimize_prompt(...)` for prompt/system behavior changes.
• `optimize_parameter(...)` for model-call hyperparameters.

7. Inspect `OptimizationResult` (`score`, `initial_score`, `history`, `optimization_id`, `get_optimized_parameters`).

Key execution details to enforce

• Prefer explicit `project_name` for Opik tracking if you are using org-level observability.
• Keep placeholders in prompts aligned with dataset fields (for example `{question}`).
• Start with `optimize_prompts="system"` or `"user"` when scope should be constrained.
• Keep `model` names in `MetaPrompt`/`reasoning` calls provider-compatible for your account.
• Validate multimodal input payloads by preserving non-empty content segments only.
• For small datasets, use `n_samples` and `n_samples_strategy` carefully; over-allocation auto-falls back to full set.

Tooling and segment-based control

• Tools can be optimized with MCP/function schema fields, not only by changing prompt wording.
• For fine-grained text updates, use `optimize_prompts` values and helper functions from `prompt_segments`:
• `extract_prompt_segments(ChatPrompt)` to inspect stable segment IDs.
• `apply_segment_updates(ChatPrompt, updates)` for deterministic edits.
• Tool optimization is distinct from prompt optimization.

Runnable examples live upstream in the Opik repo:

• https://github.com/comet-ml/opik/tree/main/sdks/opik_optimizer/src/opik_optimizer

If you need local runnable scripts, vendor the upstream examples into a `scripts/` folder and keep references one level deep.

Common mistakes to avoid

• Passing empty dataset or mismatched placeholder names.
• Mixing deprecated constructor arg `num_threads` with `n_threads`.
• Assuming tool optimization is the same as agent function-calling optimization.
• Running `ParameterOptimizer.optimize_prompt` (it raises and should not be used).

Next actions

• For in-depth behavior and per-class parameter tables: `references/algorithms.md`
• For exact `optimize_prompt` signatures, prompts, tool constraints, and result usage: `references/prompt_agent_workflow.md`
• For pattern examples and source-backed workflows: `references/example_patterns.md`

Flow

stateDiagram-v2
    [*] --> SelectObjective
    SelectObjective --> ConfigurePromptOrTools: