pydantic-models-py
by microsoftpydantic-models-py helps you create Pydantic v2 multi-model sets with Base, Create, Update, Response, and InDB variants. Use it for backend development, API request and response schemas, PATCH-friendly updates, camelCase aliases, and database-ready Python models.
This skill scores 78/100, which means it is a solid but not fully polished listing candidate. Directory users can likely install it with confidence if they want a ready-made Pydantic v2 multi-model template, but they should expect a template-focused workflow rather than a broad end-to-end automation skill.
- Explicit trigger and use case: the description says it is for Pydantic v2 multi-model patterns and specific API/database schema scenarios.
- Operationally useful template guidance: SKILL.md explains Base/Create/Update/Response/InDB variants and shows how to copy and replace placeholders.
- Concrete implementation asset: assets/template.py provides a real starter template with field examples, aliases, and update-model patterns.
- No install command or supporting scripts/references, so adoption depends on manually copying the template rather than invoking an automated workflow.
- The skill is narrow and template-centric; it helps model authorship, but offers limited support for edge cases or deeper validation/design decisions.
Overview of pydantic-models-py skill
pydantic-models-py is a Python model-generation skill for teams using Pydantic v2 and a clean multi-model API pattern. It helps you turn a rough resource idea into a consistent set of Base, Create, Update, Response, and InDB models without inventing field rules from scratch.
Use the pydantic-models-py skill when you need predictable request/response schemas for backend development, especially if you care about PATCH semantics, camelCase aliases, and a separate database shape. It is most useful when you want the same resource modeled once, then reused across API input, API output, and storage.
What pydantic-models-py is best for
The pydantic-models-py guide is strongest for CRUD-style Python services where consistency matters more than custom one-off modeling. It gives you a repeatable pattern for resources like Project, User, or Workspace, with clear boundaries between required create fields and optional update fields.
Why this skill is different
Unlike a generic prompt, pydantic-models-py install gives you a concrete template and naming scheme. That reduces drift between models, avoids accidental required fields in update payloads, and keeps aliases aligned with API conventions.
When it is a good fit
Choose pydantic-models-py for backend development when you need:
- Pydantic v2 models with explicit field validation
- a model family instead of a single schema
- camelCase API compatibility without losing Python naming
- a database-specific variant such as
InDB
How to Use pydantic-models-py skill
Install and locate the template
Install with:
npx skills add microsoft/skills --skill pydantic-models-py
For pydantic-models-py usage, start with SKILL.md and then open assets/template.py. Those two files are enough to understand the intended structure before you adapt it to your project.
Give the skill a complete resource brief
The skill works best when you name the resource and define the contract you want. A strong input includes:
- resource name in PascalCase and snake_case
- fields with types, required/optional status, and validation limits
- whether the API accepts camelCase, snake_case, or both
- whether the model is for REST, Cosmos DB, or another storage layer
Example prompt shape:
Create pydantic-models-py models for Project/project with name, description, workspace_id, status, and timestamps. name and workspace_id are required on create; description is optional; update should allow partial patching; response should expose camelCase aliases.
Read the files in the right order
For most users, the practical reading order is:
SKILL.mdfor the pattern and expected outputassets/template.pyfor the working model scaffold- Any project-specific schema or API files in your repo
That order matters because pydantic-models-py is a pattern skill, not a policy engine. You still need to map the template to your own domain rules.
Tips that improve output quality
State the field behavior up front. If workspace_id is required in create but forbidden in update, say so. If created_at and updated_at are server-managed, say that too. The pydantic-models-py skill is most effective when it can separate client input fields from derived or stored fields without guessing.
pydantic-models-py skill FAQ
Is pydantic-models-py only for Pydantic v2?
Yes, the pydantic-models-py skill is aimed at Pydantic v2-style modeling. If your project is on older Pydantic versions, expect syntax and configuration mismatches.
Do I need this skill if I already know Pydantic?
If you already know Pydantic, pydantic-models-py still helps when you want a standard multi-model layout and faster setup. It is more about consistency and installation speed than teaching the library itself.
Is it useful for backend development outside FastAPI?
Yes. The pydantic-models-py for Backend Development workflow still applies to any Python service that needs validated contracts, including internal APIs, workers, and storage adapters.
When should I not use it?
Skip pydantic-models-py if your project uses a very custom schema strategy, does not separate create/update/response shapes, or does not need alias handling and database variants. A simple single-model prompt may be enough in those cases.
How to Improve pydantic-models-py skill
Give the model family clear boundaries
The fastest way to improve pydantic-models-py results is to define what belongs in each model. Specify which fields are shared, which are create-only, which are patchable, and which are response-only. That prevents noisy output and reduces manual cleanup.
Include validation rules, not just field names
The pydantic-models-py guide works better when you provide constraints such as min/max length, enums, defaults, timestamp behavior, and whether IDs are generated by the server. These details help the generated models reflect your actual API contract instead of a generic placeholder.
Watch for alias and optionality mistakes
Common failure modes are mismatched workspace_id / workspaceId handling, update fields accidentally left required, and response models exposing fields that should stay internal. Review these first after running pydantic-models-py install output, because they affect integration quality more than style issues.
Iterate from a real endpoint
After the first output, test the model against one real endpoint or database document shape. If serialization, PATCH behavior, or storage fields feel awkward, feed that back into the pydantic-models-py skill with the exact failing field names and expected JSON. That usually improves the next pass more than asking for a broader rewrite.