# dspy-custom-module-design

Guides the creation of custom DSPy modules extending dspy.Module with state caching, input validation, and program serialization.

- 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-custom-module-design/SKILL.md
- Updated: 2026-06-23
- HTML: https://affpapa.org/en/nevibecode/skills/dspy-custom-module-design

## What it does

This skill provides reference patterns for writing custom classes on top of `dspy.Module`. It covers standard `forward()` execution, sub-module declaration, and integration with predictors such as `dspy.Predict` and `dspy.ChainOfThought`.

It also details state management inside modules. You get implementation patterns for query caching with eviction limits and execution tracking. Input guards check for empty text, while exception handlers return structured `dspy.Prediction` objects containing error descriptions instead of failing mid-pipeline.

Full program persistence via `dspy.load()` requires DSPy 2.6.0 or later. For standard module state, `save()` and `load()` write to JSON files after instantiating the class.

## Install

### Claude Code (native platform)

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

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

### Codex

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

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

### Cursor

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

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OmidZamani/dspy-skills dspy-custom-module-design
```

2. **Manually: put the files in place**
- File: `.github/skills/dspy-custom-module-design/SKILL.md`

### Gemini CLI

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

2. **Platform CLI**
```bash
gemini skills install https://github.com/OmidZamani/dspy-skills --path skills/dspy-custom-module-design
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-custom-module-design -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-custom-module-design/SKILL.md

## When to use it
- Build custom modules by subclassing dspy.Module
- Implement query caching with cache size limits
- Handle empty inputs and catch exceptions using dspy.Prediction
- Serialize and restore module parameters using JSON files

## Example prompts
- Create a custom dspy.Module classifier that validates labels and catches runtime exceptions
- Build a stateful DSPy RAG module with query caching and a maximum cache size
- Show how to serialize a DSPy pipeline and restore it from disk

## 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: 0d0a4226e26a16c9.

BEGIN THIRD-PARTY FILE 0d0a4226e26a16c9
````text
---
name: dspy-custom-module-design
version: "1.0.0"
dspy-compatibility: "3.2.1"
tags: ["production", "reasoning"]
requires-extras: []
description: Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
---

# DSPy Custom Module Design

## Goal

Design production-quality custom DSPy modules with proper architecture, state management, serialization, and testing patterns.

## When to Use

- Building reusable DSPy components
- Complex logic beyond built-in modules
- Need custom state management
- Sharing modules across projects
- Production deployment requirements

## Related Skills

- Module composition: [dspy-advanced-module-composition](../dspy-advanced-module-composition/SKILL.md)
- Signature design: [dspy-signature-designer](../dspy-signature-designer/SKILL.md)
- Optimization: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md)

## Inputs

| Input | Type | Description |
|-------|------|-------------|
| `task_description` | `str` | What the module should do |
| `components` | `list` | Sub-modules or predictors |
| `state` | `dict` | Stateful attributes |

## Outputs

| Output | Type | Description |
|--------|------|-------------|
| `custom_module` | `dspy.Module` | Production-ready module |

## Workflow

### Phase 1: Basic Module Structure

All custom modules inherit from `dspy.Module`:

```python
import dspy

class BasicQA(dspy.Module):
    """Simple question answering module."""

    def __init__(self):
        super().__init__()
        self.predictor = dspy.Predict("question -> answer")

    def forward(self, question):
        """Entry point for module execution."""
        return self.predictor(question=question)

# Usage
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
qa = BasicQA()
result = qa(question="What is Python?")
print(result.answer)
```

### Phase 2: Stateful Modules

Modules can maintain state across calls:

```python
import dspy
import logging

logger = logging.getLogger(__name__)

class StatefulRAG(dspy.Module):
    """RAG with query caching."""

    def __init__(self, cache_size=100):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought("context, question -> answer")
        self.cache = {}
        self.cache_size = cache_size

    def forward(self, question):
        # Check cache
        if question in self.cache:
            return self.cache[question]

        # Retrieve and generate
        passages = self.retrieve(question).passages
        result = self.generate(context=passages, question=question)

        # Update cache with size limit
        if len(self.cache) >= self.cache_size:
            self.cache.pop(next(iter(self.cache)))
        self.cache[question] = result

        return result
```
````
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