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Python environment management with venv, Poetry, Pipenv, pyenv, and conda. Use when user asks to "create virtual environment", "set up Poetry", "manage Python versions", "fix pip issues", "install dependencies", "create requirements.txt", or any Python environment tasks.
Configure and use GoReleaser for release automation. Use this skill when creating or editing .goreleaser.yaml files, setting up release pipelines, building cross-platform binaries, publishing to package managers (Homebrew, Scoop, AUR, nFPM packages), creating Docker images, or automating changelog generation. Supports Go, Rust, Zig, TypeScript (Bun/Deno), and Python (PyInstaller/UV/Poetry) projects. Also use when asked about releasing software, creating GitHub releases, or automating binary distribution.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing.
用于任何「运行/执行/评估代码」的请求（如 run this code、execute script、用 Python 画图、跑代码/画图/执行脚本）。通过 AgentBay SDK 创建 code_latest 沙箱，调用 run_code 执行并返回结果。支持 Python、JavaScript、R、Java。
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Sets up and operates Airbyte Agent Connectors — strongly typed Python packages for accessing 51+ third-party SaaS APIs through a unified entity-action interface. Supported services include Salesforce, HubSpot, Stripe, GitHub, Slack, Jira, Shopify, Zendesk, Google Ads, Notion, Linear, Intercom, Gong, and 36 more connectors spanning CRM, billing, payments, e-commerce, marketing, analytics, project management, helpdesk, developer tools, HR, and communication platforms. Make sure to use this skill when the user wants to connect to any SaaS API, install an airbyte-agent connector package, integrate third-party service data into a Python application or AI agent, query or search records from any supported service, or configure Airbyte MCP tools for Claude. Covers Platform Mode (Airbyte Cloud) and OSS Mode (local Python SDK).
Kubernetes Python client for programmatic cluster management. Use when working with Kubernetes API, managing pods, deployments, services, namespaces, configmaps, secrets, jobs, CRDs, EKS clusters, watching resources, automating K8s operations, or building Kubernetes controllers.
CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.
把本仓库的工程手册 Tier0 规则注入到 AI vibe coding（跨语言：Java/Python/TS/React/Vue）。
Write a marimo notebook in a Python file in the right format.
Use when building AI agents with OpenAI's Agents SDK. Triggers include creating agents, implementing tools, multi-agent handoffs, guardrails, MCP integration, tracing. Also for using LiteLLM to run agents on free/alternative models (Anthropic, Gemini). NOT for general OpenAI API usage (use openai-python SDK docs instead).
Designs deliberate practice exercises applying evidence-based learning strategies like retrieval practice, spaced repetition, and interleaving. Activate when educators need varied exercise types (fill-in-blank, debug-this, build-from-scratch, extend-code, AI-collaborative) targeting learning objectives with appropriate difficulty progression. Creates exercise sets that apply cognitive science principles to maximize retention and skill development. Use when designing practice activities for Python concepts, creating homework assignments, generating problem sets, or evaluating exercise quality.
Generate production-ready Dockerfiles with multi-stage builds, security best practices, and optimization. Use when containerizing Python applications for production deployment.
Validate code examples across the 4-Layer Teaching Method with intelligent strategy selection. Use when validating Python/Node/Rust code in book chapters. NOT for production deployment testing.
Generate comprehensive, professional project documentation structures including README, ARCHITECTURE, USER_GUIDE, DEVELOPER_GUIDE, and CONTRIBUTING files. Use when the user requests project documentation creation, asks to "document a project", needs standard documentation files, or wants to set up docs for a new repository. Adapts to Python/Go projects and OpenSource/internal contexts.
python's package managing
Apply Google Style documentation standards to Python, Go, and Terraform code. Use when writing or reviewing code that needs docstrings/comments, when asked to "document this code", "add docstrings", "follow Google Style", or when improving code documentation quality. Supports Python docstrings, Go comments, and Terraform variable/output descriptions. Enforces consistent, professional documentation standards.
Write, optimize, and debug high-performance AI compute kernels using TileLang (a Python DSL for GPU programming). Use when the user requests: (1) Writing custom GPU kernels for AI workloads (GEMM, Attention, MLA, etc.), (2) Optimizing existing TileLang code for NVIDIA, AMD, or Ascend hardware, (3) Implementing non-standard operators (like DeepSeek MLA, FlashAttention variants), (4) Debugging TileLang compilation or runtime errors, or (5) Cross-platform kernel development targeting multiple GPU vendors.
