plugins/faos-dev/skills/pydantic-models-py/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: pydantic-models-py description: 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. tags: [cloud, pydantic] --- # Pydantic Models Create Pydantic models following the multi-model pattern for clean API contracts. ## Quick Start Copy the temp
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-dev/skills/pydantic-models-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Create Pydantic models following the multi-model pattern for clean API contracts.
Copy the template from assets/template.py and replace placeholders:
{{ResourceName}} → PascalCase name (e.g., Project){{resource_name}} → snake_case name (e.g., project)| 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 |
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
class MyUpdate(BaseModel):
"""All fields optional for PATCH requests."""
name: Optional[str] = Field(None, min_length=1)
description: Optional[str] = None
class MyInDB(MyResponse):
"""Adds doc_type for Cosmos DB queries."""
doc_type: str = "my_resource"
src/backend/app/models/src/backend/app/models/__init__.pydevelopment
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU