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Python Backend

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yonatangross
python-backend

Production Python async patterns including asyncio TaskGroup, FastAPI dependency injection and middleware, SQLAlchemy 2.0 async sessions, and database connection pool tuning. Python 3.11+ runtime concerns such as ExceptionGroup, cancellation semantics, and session rollback. Use when building async services, wiring FastAPI dependencies, or tuning database connection pools. Runtime implementation layer, not the API wire contract.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namepython-backend
Stars
298
Forks
35
Bundled files
24
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 24 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Python Backend AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/python-backend
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/python-backend .claude/skills/python-backend
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Backend in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Python Backend on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Python Backend is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Backend

Patterns for building production Python backends with asyncio, FastAPI, SQLAlchemy 2.0, and connection pooling. Each category has individual rule files in rules/ loaded on-demand.

Quick Reference

CategoryRulesImpactWhen to Use
Asyncio3HIGHTaskGroup, structured concurrency, cancellation handling
FastAPI3HIGHDependencies, middleware, background tasks
SQLAlchemy3HIGHAsync sessions, relationships, migrations
Pooling3MEDIUMDatabase pools, HTTP sessions, tuning

Total: 12 rules across 4 categories. House decisions rescued from thinned files live in references/ork-delta.md; vendor material is linked, not restated (see Upstream coverage).

Quick Start

python
# FastAPI + SQLAlchemy async session
async def get_db() -> AsyncGenerator[AsyncSession, None]:
    async with async_session_factory() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise

# Reusable dependency alias (FastAPI's recommended Annotated form)
SessionDep = Annotated[AsyncSession, Depends(get_db)]

@router.get("/users/{user_id}")
async def get_user(user_id: UUID, db: SessionDep):
    result = await db.execute(select(User).where(User.id == user_id))
    return result.scalar_one_or_none()
python
# Asyncio TaskGroup with timeout
async def fetch_all(urls: list[str]) -> list[dict]:
    async with asyncio.timeout(30):
        async with asyncio.TaskGroup() as tg:
            tasks = [tg.create_task(fetch_url(url)) for url in urls]
    return [t.result() for t in tasks]

Asyncio

Modern Python asyncio patterns using structured concurrency, TaskGroup, and Python 3.11+ features.

Key Patterns

  • TaskGroup replaces gather() with structured concurrency and auto-cancellation
  • asyncio.timeout() context manager for composable timeouts
  • Semaphore for concurrency limiting (rate-limit HTTP requests)
  • except* with ExceptionGroup for handling multiple task failures
  • asyncio.to_thread() for bridging sync code to async

Key Decisions

DecisionRecommendation
Task spawningTaskGroup not gather()
Timeoutsasyncio.timeout() context manager
Concurrency limitasyncio.Semaphore
Sync bridgeasyncio.to_thread()
CancellationAlways re-raise CancelledError

FastAPI

Production-ready FastAPI patterns for lifespan, dependencies, middleware, and settings.

Key Patterns

  • Lifespan with asynccontextmanager for startup/shutdown resource management
  • Dependency injection with class-based services and Depends()
  • Middleware stack: CORS -> RequestID -> Timing -> Logging
  • Pydantic Settings with .env and field validation
  • Exception handlers wired to RFC 9457 Problem Details bodies (the body format itself is ork:api-design)

Key Decisions

DecisionRecommendation
Lifespanasynccontextmanager (not events)
DependenciesClass-based services with DI
SettingsPydantic Settings with .env
ResponseORJSONResponse for performance
HealthCheck all critical dependencies

SQLAlchemy

Async database patterns with SQLAlchemy 2.0, AsyncSession, and FastAPI integration.

Key Patterns

  • One AsyncSession per request with expire_on_commit=False
  • lazy="raise" on relationships to prevent accidental N+1 queries
  • selectinload for eager loading collections
  • Repository pattern with generic async CRUD
  • Bulk inserts chunked 1000-10000 rows for memory management

Key Decisions

DecisionRecommendation
Session scopeOne AsyncSession per request
Lazy loadinglazy="raise" + explicit loads
Eager loadingselectinload for collections
expire_on_commitFalse (prevents lazy load errors)
Poolpool_pre_ping=True

Pooling

Database and HTTP connection pooling for high-performance async Python applications.

Key Patterns

  • SQLAlchemy pool with pool_size, max_overflow, pool_pre_ping
  • Direct asyncpg pool with min_size/max_size and connection lifecycle
  • aiohttp session with TCPConnector limits and DNS caching
  • FastAPI lifespan creating and closing pools at startup/shutdown
  • Pool monitoring with Prometheus metrics

Pool Sizing Formula

pool_size = (concurrent_requests / avg_queries_per_request) * 1.5

That formula sizes one process. The fleet-level cap against the server's max_connections, and the pool alert thresholds, are in references/ork-delta.md.

Anti-Patterns (FORBIDDEN)

python
# NEVER use gather() for new code - no structured concurrency
# NEVER swallow CancelledError - breaks TaskGroup and timeout
# NEVER block the event loop with sync calls (time.sleep, requests.get)
# NEVER use global mutable state for db sessions
# NEVER skip dependency injection (create sessions in routes)
# NEVER share AsyncSession across tasks (race condition)
# NEVER use sync Session in async code (blocks event loop)
# NEVER create engine/pool per request
# NEVER forget to close pools on shutdown

Upstream coverage (do not restate)

Topics removed in the 2026-07-31 wrap-plus-delta thinning. Consult the first-party source; only the ork delta (house policy, scars, working config) belongs in this skill. Where a row says a house subset stays in a rules/ file, that file is still the authority for the OrchestKit position and the link only covers the vendor surface around it.

TopicFirst-party source
asyncio task API reference: TaskGroup vs gather(), asyncio.timeout(), ExceptionGroup and except*, to_thread(). House subset stays in rules/asyncio-taskgroup.md and rules/asyncio-cancellation.md.https://docs.python.org/3/library/asyncio-task.html
asyncio.Semaphore, Lock, Event, Queue semantics. House subset stays in rules/asyncio-structured.md; the create-once-plus-timeout rule is in references/ork-delta.md.https://docs.python.org/3/library/asyncio-sync.html
Event-loop debug mode, slow_callback_duration, detecting blocking calls (the house 100 ms threshold is in references/ork-delta.md)https://docs.python.org/3/library/asyncio-dev.html
FastAPI project scaffolding: app layout, APIRouter composition, Pydantic Settings wiring, uvicorn entry pointhttps://fastapi.tiangolo.com/tutorial/bigger-applications/
FastAPI lifespan API and startup/shutdown mechanics. House subset stays in rules/fastapi-background.md; the reverse-order teardown rule is in references/ork-delta.md.https://fastapi.tiangolo.com/advanced/events/
Starlette/FastAPI middleware API, BaseHTTPMiddleware, call_next, CORS options. House subset stays in rules/fastapi-middleware.md and references/fastapi-app-boilerplate.md; the middleware-vs-dependency split is in references/ork-delta.md.https://fastapi.tiangolo.com/tutorial/middleware/
SQLAlchemy 2.0 asyncio API: create_async_engine, async_sessionmaker, expire_on_commit, Mapped/mapped_column, with_for_update, bulk insert and update. House subset stays in rules/sqlalchemy-sessions.md, rules/sqlalchemy-relationships.md, rules/sqlalchemy-migrations.md, and references/eager-loading.md.https://docs.sqlalchemy.org/en/20/orm/extensions/asyncio.html
SQLAlchemy pool implementations and options (pool_size, max_overflow, pool_pre_ping, pool_recycle, pool_timeout). House subset stays in rules/pooling-database.md.https://docs.sqlalchemy.org/en/20/core/pooling.html
SQLAlchemy pool events (checkout, checkin, connect) for instrumentation. House subset stays in rules/pooling-tuning.md; the alert thresholds are in references/ork-delta.md.https://docs.sqlalchemy.org/en/20/core/events.html
asyncpg pool API: create_pool, min_size/max_size, max_inactive_connection_lifetime, setup hook, type codecshttps://magicstack.github.io/asyncpg/current/api/index.html
aiohttp client reference: ClientSession, TCPConnector limits, keep-alive, DNS caching, ClientTimeout. House subset stays in rules/pooling-http.md.https://docs.aiohttp.org/en/stable/client_reference.html
PostgreSQL server-side connection limits (max_connections, superuser_reserved_connections)https://www.postgresql.org/docs/current/runtime-config-connection.html
pytest-asyncio fixtures and async test setuphttps://pytest-asyncio.readthedocs.io/en/stable/reference/fixtures/index.html
Container and Kubernetes packaging, readiness/liveness probes, resource limitshttps://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/
Auth flows, password hashing, token expiry policy, security headersOwned by ork:security-patterns (src/skills/security-patterns/); the FastAPI Depends() auth chain stays in rules/fastapi-dependencies.md
RFC 9457 problem-details body format, API versioning, SSE and WebSocket wire contractsOwned by ork:api-design (src/skills/api-design/); the FastAPI exception-handler wiring stays in rules/fastapi-background.md and references/fastapi-app-boilerplate.md
Production checklists for FastAPI, asyncio, SQLAlchemy, and poolingDerivable from the sources above; no checklist restatement kept

Related Skills

  • ork:architecture-patterns - Clean architecture and layer separation
  • ork:async-jobs - Celery/ARQ for background processing
  • ork:api-design - Wire contract, RFC 9457 errors, SSE/WebSocket streaming
  • ork:database-patterns - Database schema design
  • ork:security-patterns - Auth, password hashing, token policy
  • ork:testing-integration - pytest-asyncio and httpx ASGI test setup
  • ork:devops-deployment - Docker and Kubernetes packaging

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Python Backend AI skill do?

Production Python async patterns including asyncio TaskGroup, FastAPI dependency injection and middleware, SQLAlchemy 2.0 async sessions, and database connection pool tuning. Python 3.11+ runtime concerns such as ExceptionGroup, cancellation semantics, and session rollback. Use when building async services, wiring FastAPI dependencies, or tuning database connection pools. Runtime implementation layer, not the API wire contract.

Why use Python Backend on TypingMind?

Because you install it once and use it with any model. Python Backend is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Python Backend in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/python-backend. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Python Backend?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Python Backend?

As many as you like. As long as a model supports skills, you can use Python Backend with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Python Backend AI skill free?

Yes. It is published on GitHub by yonatangross under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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