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Found 2,405 Skills
Build a project-local Kitaru adapter for an unsupported Python or TypeScript agent framework. Use when a user wants to record or replay framework-native agent runs in Kitaru, needs a custom adapter, has no supported Kitaru integration for their framework, or needs to assess whether public framework hooks and the installed Kitaru SDK can support an adapter.
Installs or verifies AMD Quark and ensures the selected Python environment has an accelerator-matched PyTorch. Applies to "install Quark", "set up Quark", "pip install amd-quark", Quark dependency or import failures, ModuleNotFoundError for quark, and Quark kernel compiler errors. Defaults to GPU and requires explicit confirmation before CPU mode. Covers PyPI universal, exactly matched native, local-wheel, and local-source installs. Does not handle standalone PyTorch requests, ONNX Runtime-only setup, or quantization.
Create, edit, and analyze Microsoft Word .docx files (reports, contracts, proposals) with styles, tables, headers/footers, template filling, content extraction, and conversion to HTML; support review workflows (comments/highlights) and inspect tracked changes via OOXML when needed using Python/Node.js (python-docx, docxtpl, mammoth.js, docx).
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, or implementing the dialectic chat endpoint for AI agents.
Expert in automating Excel workflows using Node.js (ExcelJS, SheetJS) and Python (pandas, openpyxl).
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Uses the uv Python package and project manager correctly for dependencies, venvs, and scripts. Use when creating or modifying Python projects, adding dependencies, running scripts with inline deps, managing virtual environments, pinning Python versions, running CLI tools from PyPI, setting the IDE Python interpreter, or using uv in CI (e.g. GitHub Actions) or Docker containers. Use when the user mentions uv, pyproject.toml, uv.lock, uv run, uv add, uv sync, .venv, Python interpreter, poetry, pipenv, conda, CI, Docker, GitHub Actions, or asks to use uv instead of pip or poetry.
Guide for using ty, the extremely fast Python type checker and language server. Use this when type checking Python code or setting up type checking in Python projects.
Strategic automation architecture advisor. Use when users want to plan automation solutions, evaluate their tech stack (Shopify, Zoho, HubSpot, etc.), decide between n8n vs Python/Claude Code, or need guidance on production-ready automation design. Invokes plan mode for complex architectural decisions.
Analyze animated GIF files by extracting and viewing frames as sequential video. Use when: - User mentions a GIF file path (e.g., "./demo.gif", "~/Downloads/animation.gif") - User wants to analyze or understand a GIF animation - User asks about motion, changes, or content in a GIF - User attaches or references a .gif file for analysis - User wants to examine a screen recording in GIF format - User invokes /gif slash command Keywords: "GIF", ".gif", "animation", "animated", "frames", "screen recording", "analyze gif", "gif analysis", "view gif", "gif content", "gif motion" Trigger patterns: - Natural language: "Analyze this GIF: ./demo.gif" - Slash command: `/gif <path>` or `/gif <path> <message>` When triggered, extract frames using the Python script, view frames in order, and interpret as continuous video sequence.
Best practices for Matplotlib data visualization, plotting, and creating publication-quality figures in Python