dart-python
DART Python bindings (dartpy) - nanobind, wheel building, API patterns
按技能名称、说明或 GitHub 作者匹配的结果。
DART Python bindings (dartpy) - nanobind, wheel building, API patterns
DART Python bindings (dartpy) - nanobind, wheel building, API patterns
Apply systematic performance optimization techniques for Python and Rust code: estimation + profiling, API/bulk design, algorithmic wins, cache-friendly memory layout, fewer allocations, fast paths, caching, an...
This skill fetches open tasks assigned to the user from the Linear API and generates a markdown todo list file in the project root. This skill should be used when the user asks about their work items, wants to...
Small Python utilities for math and text files.
Build real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio communication with Azure AI, in...
Clean code patterns for Azure AI Search Python SDK (azure-search-documents). Use when building search applications, creating/managing indexes, implementing agentic retrieval with knowledge bases, or working wit...
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
Shared Python best practices for LlamaFarm. Covers patterns, async, typing, testing, error handling, and security.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or...
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.
AI가 만든 모든 코드 변경사항을 reviews 폴더에 기록하고 간단한 HTML 뷰어로 웹 브라우저에서 실시간 확인할 수 있습니다. 매 수정마다 문서가 생성되고 Python 서버로 즉시 확인 가능합니다.
Use when learning Rust concepts. Keywords: mental model, how to think about ownership, understanding borrow checker, visualizing memory layout, analogy, misconception, explaining ownership, why does Rust, help...
Use when integrating crates or ecosystem questions. Keywords: E0425, E0433, E0603, crate, cargo, dependency, feature flag, workspace, which crate to use, using external C libraries, creating Python extensions,...
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or...
Runs Python with Plain configured and database access. Use for scripts, one-off commands, or interactive sessions.
Find all CPython internal headers and structs used in the codebase, particularly for profiling functionality. Use this when adding support for a new Python version to identify what CPython internals we depend o...
Compare CPython source code between two Python versions to identify changes in headers and structs. Use this when adding support for a new Python version to understand what changed between versions.
Invoke IMMEDIATELY via python script when user has a defined problem or root cause and needs solution options. Generates diverse solutions from multiple reasoning perspectives. Do NOT explore first - the script...
Invoke IMMEDIATELY via python script when user requests problem analysis or root cause investigation. Do NOT explore first - the script orchestrates the investigation.
Invoke IMMEDIATELY via python script when user requests structured reasoning for open-ended analytical questions. Do NOT explore first - the script orchestrates the thinking workflow.
Invoke IMMEDIATELY via python script to stress-test decisions and reasoning. Do NOT analyze first - the script orchestrates the critique workflow.
Invoke IMMEDIATELY via python script when user requests prompt optimization. Do NOT analyze first - invoke this skill immediately.
Invoke IMMEDIATELY via python script when user requests refactoring analysis, technical debt review, or code quality improvement. Do NOT explore first - the script orchestrates exploration.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。