agentleFS
Sign inStart free
lgzarturolgzarturo/codeconductor/.agents/skills/fastapi-pydantic-strict/SKILL.md

fastapi-pydantic-strict

Skill0 starsChanged 27 days agoMITView on GitHub

Enforces Pydantic v2 strict models, asynchronous dependency generators, and uv environment setup.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Pydantic v2 Model Configuration: Always configure models with model_config = ConfigDict(strict=True) to enforce type correctness.

    line 21
  2. Very specific:

    Environment Management: Use uv for lightning-fast package installs and lockfile consistency.

    line 23

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

FastAPI Pydantic Strict

Core Principles

  1. Pydantic v2 Model Configuration: Always configure models with model_config = ConfigDict(strict=True) to enforce type correctness.
  2. Async Dependencies: Rely on async FastAPI dependency generators (async def get_db()) to handle context managers correctly.
  3. Environment Management: Use uv for lightning-fast package installs and lockfile consistency.

Code Blueprint

from fastapi import FastAPI, Depends
from pydantic import BaseModel, ConfigDict, Field

app = FastAPI()

class StrictUser(BaseModel):
    model_config = ConfigDict(strict=True)
    
    id: int
    name: str = Field(..., min_length=2)
    email: str

@app.post("/users", response_model=StrictUser)
async def create_user(user: StrictUser):
    return user

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free