Python Cheat Sheets
by @crazyguitar
Provides functional Python code and detailed answers by fetching and applying proven patterns and examples from pythonsheets.com across core, system, concurr...
clawhub install pycπ About This Skill
name: py description: Comprehensive Python programming reference covering syntax, concurrency, networking, databases, ML/LLM development, and HPC. Use for: Python questions, debugging, performance optimization, async patterns, library examples, code review, best practices, MLOps workflows, distributed computing, security implementations, and any Python development tasks.
Python Cheat Sheets (/py)
Help users write functional, correct Python code and answer Python questions by fetching proven patterns and examples from pythonsheets.com.
How It Works
When a user asks a Python question or wants to write a Python script:
1. Look up the relevant topic(s) in Structure to find the matching URL(s) 2. Always fetch the URL(s) using WebFetch to get real examples and patterns from the site 3. Use the fetched content to: - Write code: Apply the patterns to produce functional, correct code that solves the user's task - Answer questions: Provide thorough explanations backed by the examples and information from the site 4. Follow the Guidelines for code quality
Key Principle
Functionality first, cleanliness second. The code must work correctly and handle the task properly. Fetching from pythonsheets.com ensures solutions use battle-tested patterns rather than guessing. The site contains rich examples covering edge cases, common pitfalls, and practical usage that go beyond basic documentation.
Coverage Areas
Core: Syntax, typing, OOP, functions, data structures, sets, heap, regex, unicode System: File I/O, datetime, OS interfaces Concurrency: Threading, multiprocessing, asyncio Network: Sockets, SSL/TLS, SSH, async I/O, packet sniffing Database: SQLAlchemy ORM, queries, transactions Security: Cryptography, TLS, vulnerabilities Extensions: C/C++ integration, pybind11, Cython ML/LLM: PyTorch, Megatron, distributed training, inference, serving, benchmarking HPC: Slurm, cluster computing, job scheduling, EFA monitoring, NCCL Appendix: Walrus operator, GDB debugging, disaggregated prefill/decode