Loading...
Loading...
Found 7,095 Skills
Command-line interface for Blender - A stateful command-line interface for 3D scene editing, following the same patterns as the GIMP CLI ...
Command-line interface for SeaClip-Lite - A stateless CLI for managing issues, pipelines, agents, schedules, and activity on the SeaClip-Lite project management board.
Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.
Command-line interface for Mermaid Live Editor - Create, edit, and render Mermaid diagrams via stateful project files and mermaid.ink renderer URLs. Designed for AI agents and power users who need to generate flowcharts, sequence diagrams, and other visualizations without a GUI.
Complete CLI harness for FreeCAD parametric 3D CAD modeler (258 commands). Covers ALL workbenches: Part (29 primitives + boolean + mirror + loft + sweep), Sketcher (26 cmds: geometry + constraints + editing), PartDesign (38 cmds: pad/pocket/groove/fillet/chamfer/patterns/hole/datum), Assembly (11 cmds), Mesh (16 cmds), TechDraw (15 cmds: views + dimensions + PDF/SVG), Draft (33 cmds: 2D shapes + arrays + transforms), FEM (12 cmds), CAM/CNC (10 cmds), Surface (6 cmds), Spreadsheet (7 cmds), Import (13 formats), Export (17 formats), Measure (12 cmds), Materials (21 presets). Headless FreeCAD export to STEP/IGES/STL/OBJ/DXF/PDF/glTF/3MF.
Cubox CLI is a callable personal reading memory system that enables you to search, read, and use saved content, perform semantic (RAG-based) queries, access articles, highlights, and metadata, save URLs, update content states, and retrieve annotations and structure such as folders and tags. Use this tool when a task depends on the user’s reading history or requires context from their Cubox library.
Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool. Use when user says "qzcli", "启智平台", "submit job", "stop job", "查计算组", "avail", "list jobs", "batch submit", or needs to manage distributed training jobs on a Qizhi instance.
Operate LM Studio's `lms` CLI and local/remote LM Studio servers for model discovery, server status checks, model loading, endpoint smoke tests, and downstream OpenAI-compatible wiring. Use when the user mentions LM Studio, `lms`, a local model server, `/v1/models`, a remote LM Studio host, or wants to connect another tool to LM Studio; even if they only ask to test a local OpenAI-compatible endpoint or choose the correct loaded-model identifier. Triggers on: lmstudio, lm studio, lms, local model server, LM Studio API, LM Studio endpoint, /v1/models, connect Strix to LM Studio, load model in LM Studio.
Background knowledge for droid-control workflows -- not invoked directly. Droid CLI target patterns, shortcuts, modes, and launch helpers.
Use for browser automation through camoufox-browser: open a page, inspect it with snapshot refs, click, fill, select, upload, take screenshots, and drive websites through the CLI instead of direct browser code.
Use the Helmor CLI to remote-control Helmor from the terminal. Use when the user asks to inspect Helmor data/settings, manage repositories/workspaces/sessions/files, send prompts to agents, list models, use GitHub integration, inspect scripts, migrate from Conductor, run Helmor as an MCP server, generate shell completions, quit a running app, check/install/update the Helmor CLI beta, install/update Helmor skills through the beta app flow, or needs the Helmor command reference.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.