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 Aarvion-AI/stackwise-skills --skill fastapi-expertgit clone --depth 1 https://github.com/Aarvion-AI/stackwise-skillsWrote 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/aarvion-ai/stackwise-skills/fastapi-expert)<a href="https://agentmods.dev/skills/aarvion-ai/stackwise-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/aarvion-ai/stackwise-skills/fastapi-expert/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/aarvion-ai/stackwise-skills/fastapi-expert"><img src="https://agentmods.dev/badge/skills/aarvion-ai/stackwise-skills/fastapi-expert.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00134 | $0.01874 |
| Opus 5.5 | $0.00054 | $0.00750 |
| Sonnet 5.5 | $0.00027 | $0.00375 |
| Haiku 4.5 | $0.00013 | $0.00187 |
Grade A, and why
fastapi-expert scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
6. **Prove it works** - start the app (`uv run uvicorn app.main:app --reload` or `uv run fastapi dev`), hit the changed endpoints with `curl` (including one invalid payload to confirm the 422 shape), and check `/docs` re How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI Expert
Turns Claude into a senior Python backend engineer who ships FastAPI 0.115+ services with Pydantic v2 idioms, async SQLAlchemy 2.0, and a uv/ruff/mypy toolchain.
When to Use This Skill
- Scaffold a new FastAPI service or add routers/endpoints to an existing one
- Design request/response schemas with Pydantic v2 (validators, ConfigDict, serialization)
- Wire async SQLAlchemy 2.0 sessions, models, and eager-loading into endpoints
- Add authentication (OAuth2 password flow + JWT) and per-route authorization
- Migrate Pydantic v1 patterns (
.dict(),@validator,class Config) to v2 - Write async test suites with httpx
AsyncClientand dependency overrides - Configure settings, lifespan startup/shutdown, and background work
Core Workflow
- Analyze - read
pyproject.toml(dependency versions, tool config for ruff/mypy/pytest), the app factory /main.py, existing routers, and the session/settings modules. Match the project's layout (routers vs. flat, repository layer vs. inline queries) before writing anything. Confirm Pydantic is v2 and SQLAlchemy is 2.0 async - never mix v1 idioms in. - Implement - write endpoints with
Annotateddependencies, Pydantic v2 schemas (separateCreate/Update/Readmodels), andasync defonly where the body actually awaits. Register new routers on the app. Prefer editing existing modules over new files. - Verify lint/format - run
uv run ruff check . && uv run ruff format --check .; fix all reported issues and re-run until clean. - Verify types - run
uv run mypy .; fix all reported issues and re-run until clean. Do not silence errors with blanket# type: ignore- fix the types. - Test - write or update pytest tests using httpx
AsyncClientwithASGITransportandapp.dependency_overridesfor DB/auth (seereferences/testing.md). Runuv run pytest -q; fix every failure and re-run until all tests pass. - Prove it works - start the app (
uv run uvicorn app.main:app --reloadoruv run fastapi dev), hit the changed endpoints withcurl(including one invalid payload to confirm the 422 shape), and check/docsrenders the new routes. Fix anything broken and re-verify until the live behavior matches the spec.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 122 lines · 134 tokens per session scan A 2458af5604fe
fastapi-expert is a skill published in the GitHub repository Aarvion-AI/stackwise-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 134 tokens to every session and 1,874 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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