ECC is a toolkit that organizes and improves how coding agents work through skills, memory, security checks, research practices, and related extensions. It is for developers using agents such as Claude Code, Codex, OpenCode, and Cursor.
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/affaan-m/ecc/fastapi-patternsnpx skills add affaan-m/ECC --skill fastapi-patternsgit clone --depth 1 https://github.com/affaan-m/ECCWrote 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/affaan-m/ecc/fastapi-patterns)<a href="https://agentmods.dev/skills/affaan-m/ecc/fastapi-patterns"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/fastapi-patterns.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 | $0.00035 | $0.02024 |
| Opus 5 | $0.00017 | $0.01012 |
| Sonnet 5 | $0.00007 | $0.00405 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
fastapi-patterns 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 yesterday.
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.
Copies of this mod
4 near-identical copies found in the catalogue:
- fastapi-patterns — 100% identical, 0 lines differ
- fastapi-patterns — 100% identical, 0 lines differ
- fastapi-patterns — 100% identical, 0 lines differ
- fastapi-patterns — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastAPI Patterns
Production-oriented patterns for FastAPI services.
When to Use
- Building or reviewing a FastAPI app.
- Splitting routers, schemas, dependencies, and database access.
- Writing async endpoints that call a database or external service.
- Adding authentication, authorization, OpenAPI docs, tests, or deployment settings.
- Checking a FastAPI PR for copy-pasteable examples and production risks.
How It Works
Treat the FastAPI app as a thin HTTP layer over explicit dependencies and service code:
main.pyowns app construction, middleware, exception handlers, and router registration.schemas/owns Pydantic request and response models.dependencies.pyowns database, auth, pagination, and request-scoped dependencies.services/orcrud/owns business and persistence operations.tests/overrides dependencies instead of opening production resources.
Prefer small routers and explicit response_model declarations. Keep raw ORM objects, secrets, and framework globals out of response schemas.
Project Layout
app/
|-- main.py
|-- config.py
|-- dependencies.py
|-- exceptions.py
|-- api/
| `-- routes/
| |-- users.py
| `-- health.py
|-- core/
| |-- security.py
| `-- middleware.py
|-- db/
| |-- session.py
| `-- crud.py
|-- models/
|-- schemas/
`-- tests/
Application Factory
Use a factory so tests and workers can build the app with controlled settings.
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.api.routes import health, users
from app.config import settings
from app.db.session import close_db, init_db
from app.exceptions import register_exception_handlers
@asynccontextmanager
async def lifespan(app: FastAPI):
await init_db()
yield
await close_db()
def create_app() -> FastAPI:
app = FastAPI(
title=settings.api_title,
version=settings.api_version,
lifespan=lifespan,
)
app.add_middleware(
CORSMiddleware,
allow_origins=settings.cors_origins,
allow_credentials=bool(settings.cors_origins),
allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE"],
allow_headers=["Authorization", "Content-Type"],
)
register_exception_handlers(app)
app.include_router(health.router, prefix="/health", tags=["health"])
app.include_router(users.router, prefix="/api/v1/users", tags=["users"])
return app
app = create_app()
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.
- yesterday First seen · 328 lines · 35 tokens per session scan A 6e809a690fbe
fastapi-patterns is a skill published in the GitHub repository affaan-m/ECC (248,541 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 2,024 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
accelerate
Run PyTorch training across GPUs with minimal changes.
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
implementation-strategy
Choose compatibility-aware scope for runtime and API changes in openai-agents-python. Use before initial implementation and each review-feedback batch to decide whether to patch, reset the design, preserve compatibility, or reject unsupported cases.
mem0
Mem0 SDK reference covering Python and TypeScript APIs, memory client methods, configuration, and framework integrations. Use when writing code that calls mem0 APIs, configuring memory providers, or integrating mem0 into an application.
sensitive-logging-audit
Audit and fix sensitive-data exposure through Python runtime logging in openai-agents-python. Use when reviewing logging, print, warnings, stderr, traceback, MCP names, model or tool exceptions, redaction flags, or any diagnostic path that may retain user data.
maintainer-review
Assess an openai-agents-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence…