Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jakubmikolajek/codex-skills-collection/python-fastapinpx skills add JakubMikolajek/codex-skills-collection --skill python-fastapigit clone --depth 1 https://github.com/JakubMikolajek/codex-skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jakubmikolajek/codex-skills-collection/python-fastapi)<a href="https://agentmods.dev/skills/jakubmikolajek/codex-skills-collection/python-fastapi"><img src="https://agentmods.dev/badge/skills/jakubmikolajek/codex-skills-collection/python-fastapi.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00060 | $0.01672 |
| Opus 5 | $0.00030 | $0.00836 |
| Sonnet 5 | $0.00012 | $0.00334 |
| Haiku 4.5 | $0.00006 | $0.00167 |
Grade A, and why
python-fastapi scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI Implementation Patterns
Use this skill for idiomatic FastAPI development. FastAPI's design rewards explicit typing, proper dependency injection, and clean separation of routing from business logic.
Delivery Workflow
FastAPI progress:
- [ ] Step 1: Discover project router structure, middleware, and DI conventions
- [ ] Step 2: Define request/response models and route contracts
- [ ] Step 3: Implement route handlers — thin, delegating to services
- [ ] Step 4: Wire dependencies, auth, and middleware correctly
- [ ] Step 5: Verify OpenAPI docs, error responses, and integration tests
Route Design
- Keep route handlers thin: parse input, call a service, return a response. No business logic in handlers.
- Use
APIRouterto group routes by domain; mount routers inmain.pywith a clear prefix. - Define explicit response models with
response_model=on every route — never return raw dicts. - Use
status_code=explicitly; do not accept FastAPI's implicit 200 for non-GET routes. - Annotate path and query parameters with types and
Field(...)for validation and documentation.
from fastapi import APIRouter, status
from app.schemas.document import DocumentResponse, CreateDocumentRequest
from app.services.document_service import DocumentService
router = APIRouter(prefix="/documents", tags=["documents"])
@router.post(
"/",
response_model=DocumentResponse,
status_code=status.HTTP_201_CREATED,
)
async def create_document(
payload: CreateDocumentRequest,
service: DocumentService = Depends(get_document_service),
) -> DocumentResponse:
return await service.create(payload)
Request and Response Models
- Use Pydantic v2
BaseModelfor all request and response schemas. - Keep request models (input validation) separate from response models (output shaping) and domain models (business logic).
- Use
model_config = {"from_attributes": True}on response models that map from ORM objects. - Use
Field(...)for documentation, validation constraints, and aliases. - Never expose internal domain model fields in response schemas — map explicitly.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 193 lines · 60 tokens per session scan A 5ef6f34d372b
python-fastapi is a skill published in the GitHub repository JakubMikolajek/codex-skills-collection (5 stars, last pushed 12d ago), licensed MIT. It adds 60 tokens to every session and 1,672 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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