firecrawl-sdk-patterns
Apply production-ready FireCrawl SDK patterns for TypeScript and Python. Use when implementing FireCrawl integrations, refactoring SDK usage, or establishing team coding standards for FireCrawl. Trigger with ph...
Skills correspondant au nom, à la description ou à l’auteur GitHub.
Apply production-ready FireCrawl SDK patterns for TypeScript and Python. Use when implementing FireCrawl integrations, refactoring SDK usage, or establishing team coding standards for FireCrawl. Trigger with ph...
Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the...
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space...
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets...
Remote control tmux sessions for interactive CLIs (python, gdb, etc.) by sending keystrokes and scraping pane output.
Bump the Weave Python SDK version for release. Use when preparing a new release.
Build and publish the Weave Python SDK to PyPI. Use when releasing a new version.
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.
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,...
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...
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 this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job'...
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...
Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-spec...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.