Agent skill

Python Patterns

by affaan-m in affaan-m/ECC

Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar.

MITAuto-check passedDevelopment

Install Python Patterns

skills CLI
$ npx skills add affaan-m/ECC --skill python-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC python-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/tr/skills/python-patterns .claude/skills/python-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
python-patterns
GitHub stars
274k
Used in
1 other repo
Token cost
~4.3k tokens
SKILL.md length
285 words
Files
1
Skills in repo
657
Repo updated
First seen
Licence
MIT

At a glance

Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar.

  • Works in 3 steps: Okunabilirlik Önemlidir → Açık, Örtük Olandan Daha İyidir → EAFP - Affederek Sormaktansa İzin…
  • Tasks that involve Type safety
  • SKILL.md covers Ne Zaman Etkinleştirmeli, Temel Prensipler, Type Hint'ler and Hata İşleme Desenleri, plus 4 more sections
  • Calls black, ruff and mypy

What it does

Python Patterns is an agent skill from affaan-m/ECC. Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar.

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Type safety. It works with Python. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Tasks that involve Type safety

Example prompts

  • “/python-patterns”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Okunabilirlik Önemlidir
  2. Açık, Örtük Olandan Daha İyidir
  3. EAFP - Affederek Sormaktansa İzin İstemek Daha Kolaydır

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • black
    • ruff
    • mypy
    • pytest

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Python Patterns loads about 4.3k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 285 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 285 words, ~4,257 tokens.

Download SKILL.mdSave it as .claude/skills/python-patterns/SKILL.md (or your agent's skills folder).
name
python-patterns
description
Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar.
origin
ECC

Python Geliştirme Desenleri

Sağlam, verimli ve bakımı kolay uygulamalar oluşturmak için idiomatic Python desenleri ve en iyi uygulamalar.

Ne Zaman Etkinleştirmeli

  • Yeni Python kodu yazarken
  • Python kodunu gözden geçirirken
  • Mevcut Python kodunu refactor ederken
  • Python paketleri/modülleri tasarlarken

Temel Prensipler

1. Okunabilirlik Önemlidir

Python okunabilirliği önceliklendirir. Kod açık ve anlaşılması kolay olmalıdır.

python
# İyi: Açık ve okunabilir
def get_active_users(users: list[User]) -> list[User]:
    """Sağlanan listeden sadece aktif kullanıcıları döndür."""
    return [user for user in users if user.is_active]


# Kötü: Zeki ama kafa karıştırıcı
def get_active_users(u):
    return [x for x in u if x.a]
2. Açık, Örtük Olandan Daha İyidir

Sihirden kaçının; kodunuzun ne yaptığı konusunda açık olun.

python
# İyi: Açık yapılandırma
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Kötü: Gizli yan etkiler
import some_module
some_module.setup()  # Bu ne yapıyor?
3. EAFP - Affederek Sormaktansa İzin İstemek Daha Kolaydır

Python, koşulları kontrol etmek yerine exception handling'i tercih eder.

python
# İyi: EAFP stili
def get_value(dictionary: dict, key: str, default_value: Any = None) -> Any:
    try:
        return dictionary[key]
    except KeyError:
        return default_value

# Kötü: LBYL (Atlamadan Önce Bak) stili
def get_value(dictionary: dict, key: str, default_value: Any = None) -> Any:
    if key in dictionary:
        return dictionary[key]
    else:
        return default_value

Type Hint'ler

Temel Type Annotation'lar
python
from typing import Optional, List, Dict, Any

def process_user(
    user_id: str,
    data: Dict[str, Any],
    active: bool = True
) -> Optional[User]:
    """Bir kullanıcıyı işle ve güncellenmiş User'ı veya None döndür."""
    if not active:
        return None
    return User(user_id, data)
Modern Type Hint'ler (Python 3.9+)
python
# Python 3.9+ - Built-in tipleri kullan
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Python 3.8 ve öncesi - typing modülünü kullan
from typing import List, Dict

def process_items(items: List[str]) -> Dict[str, int]:
    return {item: len(item) for item in items}
Type Alias'ları ve TypeVar
python
from typing import TypeVar, Union

# Karmaşık tipler için type alias
JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]

def parse_json(data: str) -> JSON:
    return json.loads(data)

# Generic tipler
T = TypeVar('T')

def first(items: list[T]) -> T | None:
    """İlk öğeyi döndür veya liste boşsa None döndür."""
    return items[0] if items else None
Protocol Tabanlı Duck Typing
python
from typing import Protocol

class Renderable(Protocol):
    def render(self) -> str:
        """Nesneyi string'e render et."""

def render_all(items: list[Renderable]) -> str:
    """Renderable protocol'ünü implement eden tüm öğeleri render et."""
    return "\n".join(item.render() for item in items)

Hata İşleme Desenleri

Spesifik Exception Handling
python
# İyi: Spesifik exception'ları yakala
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except FileNotFoundError as e:
        raise ConfigError(f"Config file not found: {path}") from e
    except json.JSONDecodeError as e:
        raise ConfigError(f"Invalid JSON in config: {path}") from e

# Kötü: Bare except
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except:
        return None  # Sessiz hata!
Exception Chaining
python
def process_data(data: str) -> Result:
    try:
        parsed = json.loads(data)
    except json.JSONDecodeError as e:
        # Traceback'i korumak için exception'ları zincirleme
        raise ValueError(f"Failed to parse data: {data}") from e
Özel Exception Hiyerarşisi
python
class AppError(Exception):
    """Tüm uygulama hataları için base exception."""
    pass

class ValidationError(AppError):
    """Input validation başarısız olduğunda raise edilir."""
    pass

class NotFoundError(AppError):
    """İstenen kaynak bulunamadığında raise edilir."""
    pass

# Kullanım
def get_user(user_id: str) -> User:
    user = db.find_user(user_id)
    if not user:
        raise NotFoundError(f"User not found: {user_id}")
    return user

Context Manager'lar

Kaynak Yönetimi
python
# İyi: Context manager'ları kullanma
def process_file(path: str) -> str:
    with open(path, 'r') as f:
        return f.read()

# Kötü: Manuel kaynak yönetimi
def process_file(path: str) -> str:
    f = open(path, 'r')
    try:
        return f.read()
    finally:
        f.close()
Özel Context Manager'lar
python
from contextlib import contextmanager

@contextmanager
def timer(name: str):
    """Bir kod bloğunu zamanlamak için context manager."""
    start = time.perf_counter()
    yield
    elapsed = time.perf_counter() - start
    print(f"{name} took {elapsed:.4f} seconds")

# Kullanım
with timer("data processing"):
    process_large_dataset()
Context Manager Class'ları
python
class DatabaseTransaction:
    def __init__(self, connection):
        self.connection = connection

    def __enter__(self):
        self.connection.begin_transaction()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is None:
            self.connection.commit()
        else:
            self.connection.rollback()
        return False  # Exception'ları suppress etme

# Kullanım
with DatabaseTransaction(conn):
    user = conn.create_user(user_data)
    conn.create_profile(user.id, profile_data)

Comprehension'lar ve Generator'lar

List Comprehension'ları
python
# İyi: Basit dönüşümler için list comprehension
names = [user.name for user in users if user.is_active]

# Kötü: Manuel döngü
names = []
for user in users:
    if user.is_active:
        names.append(user.name)

# Karmaşık comprehension'lar genişletilmelidir
# Kötü: Çok karmaşık
result = [x * 2 for x in items if x > 0 if x % 2 == 0]

# İyi: Bir generator fonksiyonu kullan
def filter_and_transform(items: Iterable[int]) -> list[int]:
    result = []
    for x in items:
        if x > 0 and x % 2 == 0:
            result.append(x * 2)
    return result
Generator Expression'ları
python
# İyi: Lazy evaluation için generator
total = sum(x * x for x in range(1_000_000))

# Kötü: Büyük ara liste oluşturur
total = sum([x * x for x in range(1_000_000)])
Generator Fonksiyonları
python
def read_large_file(path: str) -> Iterator[str]:
    """Büyük bir dosyayı satır satır oku."""
    with open(path) as f:
        for line in f:
            yield line.strip()

# Kullanım
for line in read_large_file("huge.txt"):
    process(line)

Data Class'lar ve Named Tuple'lar

Data Class'lar
python
from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class User:
    """Otomatik __init__, __repr__ ve __eq__ ile User entity."""
    id: str
    name: str
    email: str
    created_at: datetime = field(default_factory=datetime.now)
    is_active: bool = True

# Kullanım
user = User(
    id="123",
    name="Alice",
    email="alice@example.com"
)
Validation ile Data Class'lar
python
@dataclass
class User:
    email: str
    age: int

    def __post_init__(self):
        # Email formatını validate et
        if "@" not in self.email:
            raise ValueError(f"Invalid email: {self.email}")
        # Yaş aralığını validate et
        if self.age < 0 or self.age > 150:
            raise ValueError(f"Invalid age: {self.age}")
Named Tuple'lar
python
from typing import NamedTuple

class Point(NamedTuple):
    """Immutable 2D nokta."""
    x: float
    y: float

    def distance(self, other: 'Point') -> float:
        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5

# Kullanım
p1 = Point(0, 0)
p2 = Point(3, 4)
print(p1.distance(p2))  # 5.0

Decorator'lar

Fonksiyon Decorator'ları
python
import functools
import time

def timer(func: Callable) -> Callable:
    """Fonksiyon yürütmesini zamanlamak için decorator."""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} took {elapsed:.4f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)

# slow_function() yazdırır: slow_function took 1.0012s
Parametreli Decorator'lar
python
def repeat(times: int):
    """Bir fonksiyonu birden çok kez tekrarlamak için decorator."""
    def decorator(func: Callable) -> Callable:
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            results = []
            for _ in range(times):
                results.append(func(*args, **kwargs))
            return results
        return wrapper
    return decorator

@repeat(times=3)
def greet(name: str) -> str:
    return f"Hello, {name}!"

# greet("Alice") döndürür ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"]
Class Tabanlı Decorator'lar
python
class CountCalls:
    """Bir fonksiyonun kaç kez çağrıldığını sayan decorator."""
    def __init__(self, func: Callable):
        functools.update_wrapper(self, func)
        self.func = func
        self.count = 0

    def __call__(self, *args, **kwargs):
        self.count += 1
        print(f"{self.func.__name__} has been called {self.count} times")
        return self.func(*args, **kwargs)

@CountCalls
def process():
    pass

# Her process() çağrısı çağrı sayısını yazdırır

Eşzamanlılık Desenleri

I/O-Bound Görevler için Threading
python
import concurrent.futures
import threading

def fetch_url(url: str) -> str:
    """Bir URL fetch et (I/O-bound operasyon)."""
    import urllib.request
    with urllib.request.urlopen(url) as response:
        return response.read().decode()

def fetch_all_urls(urls: list[str]) -> dict[str, str]:
    """Thread'ler kullanarak birden fazla URL'yi eşzamanlı fetch et."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        future_to_url = {executor.submit(fetch_url, url): url for url in urls}
        results = {}
        for future in concurrent.futures.as_completed(future_to_url):
            url = future_to_url[future]
            try:
                results[url] = future.result()
            except Exception as e:
                results[url] = f"Error: {e}"
    return results
CPU-Bound Görevler için Multiprocessing
python
def process_data(data: list[int]) -> int:
    """CPU-yoğun hesaplama."""
    return sum(x ** 2 for x in data)

def process_all(datasets: list[list[int]]) -> list[int]:
    """Birden fazla process kullanarak birden fazla dataset işle."""
    with concurrent.futures.ProcessPoolExecutor() as executor:
        results = list(executor.map(process_data, datasets))
    return results
Eşzamanlı I/O için Async/Await
python
import asyncio

async def fetch_async(url: str) -> str:
    """Asenkron olarak bir URL fetch et."""
    import aiohttp
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def fetch_all(urls: list[str]) -> dict[str, str]:
    """Birden fazla URL'yi eşzamanlı fetch et."""
    tasks = [fetch_async(url) for url in urls]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return dict(zip(urls, results))

Paket Organizasyonu

Standart Proje Düzeni
myproject/
├── src/
│   └── mypackage/
│       ├── __init__.py
│       ├── main.py
│       ├── api/
│       │   ├── __init__.py
│       │   └── routes.py
│       ├── models/
│       │   ├── __init__.py
│       │   └── user.py
│       └── utils/
│           ├── __init__.py
│           └── helpers.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_api.py
│   └── test_models.py
├── pyproject.toml
├── README.md
└── .gitignore
Import Konvansiyonları
python
# İyi: Import sırası - stdlib, third-party, local
import os
import sys
from pathlib import Path

import requests
from fastapi import FastAPI

from mypackage.models import User
from mypackage.utils import format_name

# İyi: Otomatik import sıralama için isort kullanın
# pip install isort
Paket Export'ları için init.py
python
# mypackage/__init__.py
"""mypackage - Örnek bir Python paketi."""

__version__ = "1.0.0"

# Ana class/fonksiyonları paket seviyesinde export et
from mypackage.models import User, Post
from mypackage.utils import format_name

__all__ = ["User", "Post", "format_name"]

Bellek ve Performans

Bellek Verimliliği için slots Kullanma
python
# Kötü: Normal class __dict__ kullanır (daha fazla bellek)
class Point:
    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

# İyi: __slots__ bellek kullanımını azaltır
class Point:
    __slots__ = ['x', 'y']

    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y
Büyük Veri için Generator
python
# Kötü: Bellekte tam liste döndürür
def read_lines(path: str) -> list[str]:
    with open(path) as f:
        return [line.strip() for line in f]

# İyi: Satırları birer birer yield eder
def read_lines(path: str) -> Iterator[str]:
    with open(path) as f:
        for line in f:
            yield line.strip()
Döngülerde String Birleştirmekten Kaçının
python
# Kötü: String immutability nedeniyle O(n²)
result = ""
for item in items:
    result += str(item)

# İyi: join kullanarak O(n)
result = "".join(str(item) for item in items)

# İyi: Oluşturma için StringIO kullanma
from io import StringIO

buffer = StringIO()
for item in items:
    buffer.write(str(item))
result = buffer.getvalue()

Python Tooling Entegrasyonu

Temel Komutlar
bash
# Kod formatlama
black .
isort .

# Linting
ruff check .
pylint mypackage/

# Type checking
mypy .

# Test
pytest --cov=mypackage --cov-report=html

# Güvenlik taraması
bandit -r .

# Dependency yönetimi
pip-audit
safety check
pyproject.toml Yapılandırması
toml
[project]
name = "mypackage"
version = "1.0.0"
requires-python = ">=3.9"
dependencies = [
    "requests>=2.31.0",
    "pydantic>=2.0.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "black>=23.0.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]

[tool.black]
line-length = 88
target-version = ['py39']

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
python_version = "3.9"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "--cov=mypackage --cov-report=term-missing"

Hızlı Referans: Python İfadeleri

İfadeAçıklama
EAFPAffederek Sormaktansa İzin İstemek Daha Kolay
Context manager'larKaynak yönetimi için with kullan
List comprehension'larBasit dönüşümler için
Generator'larLazy evaluation ve büyük dataset'ler için
Type hint'lerFonksiyon signature'larını annotate et
Dataclass'larAuto-generated metodlarla veri container'ları için
__slots__Bellek optimizasyonu için
f-string'lerString formatlama için (Python 3.6+)
pathlib.PathPath operasyonları için (Python 3.4+)
enumerateDöngülerde index-element çiftleri için

Kaçınılması Gereken Anti-Desenler

python
# Kötü: Mutable default argümanlar
def append_to(item, items=[]):
    items.append(item)
    return items

# İyi: None kullan ve yeni liste oluştur
def append_to(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

# Kötü: type() ile tip kontrolü
if type(obj) == list:
    process(obj)

# İyi: isinstance kullan
if isinstance(obj, list):
    process(obj)

# Kötü: None ile == ile karşılaştırma
if value == None:
    process()

# İyi: is kullan
if value is None:
    process()

# Kötü: from module import *
from os.path import *

# İyi: Açık import'lar
from os.path import join, exists

# Kötü: Bare except
try:
    risky_operation()
except:
    pass

# İyi: Spesifik exception
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")

Unutmayın: Python kodu okunabilir, açık ve en az sürpriz ilkesine uygun olmalıdır. Şüphe duyduğunuzda, açıklığı zekiceden öncelikli kılın.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/tr/skills/python-patterns of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Python Patterns

What does Python Patterns do?

Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar. Python Patterns is an agent skill from affaan-m/ECC. Pythonic idiomlar, PEP 8 standartları, type hint'ler ve sağlam, verimli ve bakımı kolay Python uygulamaları oluşturmak için en iyi uygulamalar.

When should I use Python Patterns?

Python Patterns fits situations like: tasks that involve Type safety.

How do I install Python Patterns in Claude Code?

Run `npx skills add affaan-m/ECC --skill python-patterns -a claude-code`. Or copy the skill folder (docs/tr/skills/python-patterns in affaan-m/ECC) into .claude/skills/python-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Python Patterns in Codex?

Run `npx skills add affaan-m/ECC --skill python-patterns -a codex`. Or copy the skill folder (docs/tr/skills/python-patterns in affaan-m/ECC) into .agents/skills/python-patterns in your project. Codex loads it when a task matches its description.

Can I use Python Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill python-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-patterns, .gemini/skills/python-patterns, .github/skills/python-patterns and .opencode/skills/python-patterns in your project.

What does Python Patterns need to run?

Going by SKILL.md and its folder, Python Patterns needs the command-line tools its instructions call (black, ruff, mypy and pytest). Our summary lists: Python 3.

Does Python Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Python Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Python Patterns use?

Python Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Python Patterns use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Python Patterns?

Skills that share tags, products or a category with Python Patterns: Kedro Babysit (kedro-org/kedro, 11k stars), Dignified Python Standards (docling-project/docling, 68k stars), Rust Ffi (ariebovenberg/whenever, 2.4k stars) and Diataxis Docs Writer (calf-ai/calfkit-sdk, 149 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Python Patterns?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 274,360 GitHub stars. The repository holds 657 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.