# dspy-simba-optimizer

This skill guides SIMBA optimization for DSPy programs, using numeric scores to refine prompts and demonstrations. Use it when you can measure task quality with a metric.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 124
- Author: OmidZamani
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-simba-optimizer/SKILL.md
- Updated: 2026-06-23
- HTML: https://affpapa.org/en/nevibecode/skills/dspy-simba-optimizer

## What it does

SIMBA improves a DSPy program through mini-batch iterations. It looks for examples where outputs vary, then uses them to form reflective rules or select successful demonstrations. The result is an optimized `dspy.Module` that can be saved to a file.

You provide a program, training examples, and a metric that returns a numeric score. The skill covers batch size, optimization steps, candidate count, and temperature settings. The metric matters: if it cannot assign numeric quality scores, SIMBA has no score to guide its optimization.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/dspy-simba-optimizer/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/dspy-simba-optimizer/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/dspy-simba-optimizer/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/dspy-simba-optimizer/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OmidZamani/dspy-skills dspy-simba-optimizer
```

2. **Manually: put the files in place**
- File: `.github/skills/dspy-simba-optimizer/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/OmidZamani/dspy-skills --path skills/dspy-simba-optimizer
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/dspy-simba-optimizer/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-simba-optimizer/SKILL.md

## When to use it
- Optimize a DSPy program against a numeric metric
- Identify challenging cases from output variation
- Build reflective rules for prompts
- Add successful demonstrations to a program

## Example prompts
- Optimize my DSPy program with SIMBA using this metric and training set.
- Set up mini-batch SIMBA optimization and save the result as `qa_simba.json`.
- Evaluate the agent before and after SIMBA, then save it as `research_agent_simba.json`.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 68824e74ea82f29c.

BEGIN THIRD-PARTY FILE 68824e74ea82f29c
````text
---
name: dspy-simba-optimizer
version: "1.0.0"
dspy-compatibility: "3.2.1"
tags: ["optimizer"]
requires-extras: []
description: Use for SIMBA optimization, mini-batch introspective optimization, self-reflective rules, stochastic ascent, and numeric-metric optimization.
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
---

# DSPy SIMBA Optimizer

## Goal

Optimize DSPy programs using stochastic mini-batch sampling, output variability, self-reflective rules, and successful demonstrations.

## When to Use

- Need lighter-weight alternative to GEPA
- Have a numeric metric that captures task quality
- Want introspective rules and demonstrations
- Budget-conscious optimization (fewer eval calls)
- Programs where few-shot examples aren't critical

## Related Skills

- Alternative optimizers: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md), [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md)
- Agent optimization: [dspy-react-agent-builder](../dspy-react-agent-builder/SKILL.md)
- Evaluation: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md)

## Inputs

| Input | Type | Description |
|-------|------|-------------|
| `program` | `dspy.Module` | Program to optimize |
| `trainset` | `list[dspy.Example]` | Training examples |
| `metric` | `callable` | Returns a numeric score |
| `max_steps` | `int` | Number of optimization steps |
| `bsize` | `int` | Mini-batch size |

## Outputs

| Output | Type | Description |
|--------|------|-------------|
| `optimized_program` | `dspy.Module` | SIMBA-optimized program |

## Workflow

### Phase 1: Understand SIMBA

**SIMBA** (Stochastic Introspective Mini-Batch Ascent):
- Iterative prompt optimization with mini-batch sampling
- Identifies challenging examples with high output variability
- Generates self-reflective rules or adds successful demonstrations
- Uses the configured LM or `prompt_model` for introspection
- More exploratory than basic bootstrap optimization

**Comparison:**
- **MIPROv2**: Best accuracy, lots of data
- **GEPA**: Agentic systems, expensive
- **SIMBA**: Mini-batch introspection, budget-friendly
- **Bootstrap**: Simplest, demo-based

### Phase 2: Basic SIMBA Optimization

```python
import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

# Program to optimize
class QAPipeline(dspy.Module):
    def __init__(self):
        self.generate = dspy.ChainOfThought("question -> answer")

    def forward(self, question):
        return self.generate(question=question)

# Metric returns a numeric score
def qa_metric(example, pred, trace=None):
    correct = example.answer.lower() in pred.answer.lower()
    return 1.0 if correct else 0.0

# SIMBA optimizer
optimizer = dspy.SIMBA(
    metric=qa_metric,
    max_steps=10,  # Optimization iterations
    bsize=5  # Mini-batch size
)

program = QAPipeline()
compiled = optimizer.compile(program, trainset=trainset)
compiled.save("qa_simba.json")
```

### Phase 3: SIMBA with a Nuanced Numeric Metric

Use a graded numeric metric when exact match is too coarse:

```python
import dspy

def detailed_metric(example, pred, trace=None):
    """Return a graded numeric score."""
````
END THIRD-PARTY FILE 68824e74ea82f29c

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