pydantic-models-py
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Best use case
pydantic-models-py is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Teams using pydantic-models-py should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pydantic-models-py/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pydantic-models-py Compares
| Feature / Agent | pydantic-models-py | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Pydantic Models
Create Pydantic models following the multi-model pattern for clean API contracts.
## Quick Start
Copy the template from [assets/template.py](assets/template.py) and replace placeholders:
- `{{ResourceName}}` → PascalCase name (e.g., `Project`)
- `{{resource_name}}` → snake_case name (e.g., `project`)
## Multi-Model Pattern
| Model | Purpose |
|-------|---------|
| `Base` | Common fields shared across models |
| `Create` | Request body for creation (required fields) |
| `Update` | Request body for updates (all optional) |
| `Response` | API response with all fields |
| `InDB` | Database document with `doc_type` |
## camelCase Aliases
```python
class MyModel(BaseModel):
workspace_id: str = Field(..., alias="workspaceId")
created_at: datetime = Field(..., alias="createdAt")
class Config:
populate_by_name = True # Accept both snake_case and camelCase
```
## Optional Update Fields
```python
class MyUpdate(BaseModel):
"""All fields optional for PATCH requests."""
name: Optional[str] = Field(None, min_length=1)
description: Optional[str] = None
```
## Database Document
```python
class MyInDB(MyResponse):
"""Adds doc_type for Cosmos DB queries."""
doc_type: str = "my_resource"
```
## Integration Steps
1. Create models in `src/backend/app/models/`
2. Export from `src/backend/app/models/__init__.py`
3. Add corresponding TypeScript typesRelated Skills
adapting-transfer-learning-models
This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.
training-machine-learning-models
Build train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to "train model" or "evalua... Trigger with relevant phrases based on skill purpose.
evaluating-machine-learning-models
This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
deploying-machine-learning-models
This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."
explaining-machine-learning-models
Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
optimizing-deep-learning-models
This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".
building-classification-models
This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
avalonia-viewmodels-zafiro
Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.
pydanticai-docs
Use this skill for requests related to Pydantic AI framework - building agents, tools, dependencies, structured outputs, and model integrations.
when-developing-ml-models-use-ml-expert
Specialized ML model development, training, and deployment workflow
backend-models
Define and configure database models with proper naming, relationships, timestamps, data types, constraints, and validation. Use this skill when creating or editing model files in app/Models/, Eloquent model classes, model relationships (hasMany, belongsTo, etc.), database table structures, model attributes and casts, model factories, or seeders. Use when working on model validation logic, database constraints, foreign key relationships, indexes, scopes, accessors, mutators, or any ORM-related model configuration.
py-pydantic-patterns
Pydantic v2 patterns for validation and serialization. Use when creating schemas, validating data, or working with request/response models.