Results for “python3”

Python language (3.10-3.14). Covers typing, async, and modern patterns. Use when writing Python applications. USE WHEN: user mentions "python", "type hints", "dataclasses", "async/await", asks about "asyncio", "context managers", "match statement", "walrus operator", "PEP 695", "type parameter", "generic" DO NOT USE FOR: FastAPI framework - use `backend-fastapi` skill instead DO NOT USE FOR: Django framework - use Django-specific skill DO NOT USE FOR: Package management - use `python-packaging` skill DO NOT USE FOR: Linting/type checking config - use `python-quality` skill

Generates type-safe, async-first Python 3.11+ code with full type annotations, mypy strict mode, pytest test suites, and validation via black and ruff.

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

Write and review modern Python 3.12+ code with async patterns, performance optimization, and production-ready practices using the latest ecosystem tools.

Execute Python code in a safe sandboxed environment with 100+ pre-installed libraries for data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, and automation.

Use when implementing python functionality with production-grade patterns and safeguards.

Debug Python with pdb, breakpoint(), post-mortem inspection, and debugpy remote attach.

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.

Python programming patterns and best practices

Applies senior Python rules for typing, async, APIs, persistence, tests, packaging, and architecture.

Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants for clean API contracts in Python applications using Pydantic v2.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Python development best practices including PEP 8 style guidelines, type hints, docstring conventions, and common patterns. Use when writing or modifying Python code.

Debug Python: pdb REPL + debugpy remote (DAP).

Test Python services with pytest, async coverage, monkeypatch, and boundary-focused fakes. Use when writing Python tests, fixtures, async tests, regression tests, or dependency-isolated verification.

Solve single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including Pareto front analysis, constraint handling, and benchmarking on standard test problems.

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

Use for writing, reviewing, debugging, testing, or validating Python mpmath arbitrary-precision numerical code. Trigger on mpf, mpc, mp.dps, workdps, interval arithmetic, high-precision quadrature, root finding, special functions, matrices, inverse transforms, or precision/convergence failures. Do not use for ordinary NumPy vectorization, SymPy symbolic manipulation, decimal currency arithmetic, or machine-float code with no precision requirement.

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Shape or refine the existing bead or caller intent without creating a second planning artifact. Triggers: "plan", "discover and plan", "shape this goal".

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Enforces principal-engineer-level Python code quality: formatting, type safety, idiomatic patterns, class and function design, and anti-pattern prevention.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.

Master Test Architect and Quality Advisor. Use when the user asks to talk to Murat or requests the Test Architect.

Use when inferring maximum-likelihood phylogenetic trees, selecting substitution models, running bootstrap support analyses, or performing partitioned phylogenetic analyses on sequence alignments.

Configure and maintain Python projects, dependencies, packaging, environments, linting, formatting, type checking, testing, builds, and publishing. Use for pyproject.toml, an existing repository toolchain, or repository-level Python tooling work, not for a standalone script.

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Generate traceability matrix and quality gate decision. Use when the user says "lets create traceability matrix" or "I want to analyze test coverage"

Debug Python code interactively with pdb and debugpy, covering local breakpoints, post-mortem analysis, and remote attachment to running processes.

Write, review, debug, or test Python asyncio code involving coroutines, Tasks, TaskGroups, cancellation, timeouts, queues, synchronization, and blocking-call boundaries.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

在Vue3环境中，将包含多个字典（对象）的列表转换为仅包含值的列表（[[],[]]）或包含索引的字典列表（[{0:{}}, {1:{}}]）。

Use this when designing Python scripts for automation, batch processing, file operations, data transformation, or internal workflows that need deterministic structure and safe environment handling.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Expand test automation coverage for codebase. Use when user says "lets expand test coverage" or "I want to automate tests"