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 skills add nimadorostkar/Claude-Skills-collection --skill fastapigit clone --depth 1 https://github.com/nimadorostkar/Claude-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/nimadorostkar/claude-skills-collection/fastapi)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/fastapi"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/fastapi/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/fastapi"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/fastapi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00089 | $0.01065 |
| Opus 5.5 | $0.00036 | $0.00426 |
| Sonnet 5.5 | $0.00018 | $0.00213 |
| Haiku 4.5 | $0.00009 | $0.00106 |
Grade A, and why
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 15d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI
Purpose
Build FastAPI services that use the framework's strengths — declarative validation and dependency injection — without falling into its two standard traps: blocking calls inside async def, and business logic in the route handler.
When to Use
- Building or reviewing a FastAPI application.
- Structuring dependencies, authentication, and database sessions.
- Diagnosing latency that appears only under concurrency.
- Writing tests for FastAPI endpoints.
Capabilities
- Route and router organization.
- Pydantic v2 models for request, response, and settings.
- Dependency injection with scoped lifecycles.
- Async SQLAlchemy sessions, correctly scoped per request.
- Authentication and authorization dependencies.
- Testing with
httpx.AsyncClientand dependency overrides.
Inputs
- The API contract and the data layer.
- Whether the workload is I/O-bound (nearly always) or CPU-bound.
Outputs
- Thin route handlers delegating to service functions.
- Response models that control exactly what is serialized.
- A test suite that overrides dependencies rather than mocking internals.
Workflow
- Define the schemas — Separate request, response, and internal models. Never return an ORM object directly; a
response_modelis your defense against leaking a password hash. - Build the dependencies — Database session, current user, feature flags. These are the injection points that make the app testable.
- Keep handlers thin — Parse, authorize, delegate, return. Business logic lives in a service module that knows nothing about HTTP.
- Get async right — In an
async defhandler, every I/O call must be awaited. A blocking call there stalls the entire event loop, not just that request. - Test through the app —
httpx.AsyncClientwithapp.dependency_overridesgives you real routing, real validation, and a fake database.
Best Practices
- A blocking call inside
async def(a sync DB driver,requests,time.sleep) blocks every concurrent request on that worker. If a handler must call blocking code, define it asdef— FastAPI runs it in a thread pool. - Always set
response_model. Without it, whatever your service returns is what the client sees, including fields you added last week. - Use
Annotated[Session, Depends(get_session)]— it keeps signatures readable and reusable. - Validate settings with
pydantic-settingsat startup. Fail to boot on a bad config rather than at 3am on the first request that touches it. BackgroundTasksruns in the same process and dies with it. For anything that must not be lost, use a real queue.- Mount routers by domain, not by HTTP verb.
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.
- 15d ago Changed · +55 tokens per session ec07bc9ce2bf
- 1mo ago First seen · 102 lines · 34 tokens per session scan A a412a14083b2
fastapi is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (32 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,065 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.
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