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Found 6,258 Skills
Academic-first Draw.io figure skill for papers, theses, IEEE-style diagrams, architecture figures, workflows, roadmaps, formulas, and publication-ready visualizations. Use when users ask to draw, redraw, replicate, edit, or export diagrams for academic papers or technical documents. Creates offline .drawio + .spec.yaml + .arch.json bundles, exports SVG locally, uses draw.io Desktop CLI for embedded SVG/PNG/PDF/JPG, supports style presets, self-check review loops, and diagrams.net URL fallback without requiring MCP.
Turns AI-generated demo UIs into real usable product workflows. Use when building, reviewing, or finishing apps, dashboards, forms, CRUD flows, onboarding, checkout, settings, auth-like flows, or any interface that must work beyond a static mockup.
This skill should be used when the user asks to "search YouTube", "find videos about", "get a transcript", "download subtitles", "extract audio from YouTube", "scan a channel", "research a topic on YouTube", "get video metadata", "what videos exist about", "download YouTube audio", "YouTube research", "summarize this video", "what is this video about", "pull captions from", "grab the audio from", or provides a YouTube/Vimeo/video URL and wants to extract information from it. Also triggers on "batch download transcripts", "analyze a channel", or any multi-video research workflow.
Builds territory planning workflows in CARTO combining territory balancing and location allocation. Triggers when the user mentions territory balancing, territory planning, sales territories, service zones, workload distribution, balanced territories, location allocation, facility placement, optimal locations, maximize coverage, minimize cost, minimize travel distance, depot placement, hub placement, warehouse siting, response time optimization, demand coverage, or wants to divide an area into balanced regions or find optimal facility locations.
Provides expertise on Chroma Cloud integration for semantic search and hybrid search applications. Use when the user is working with Chroma Cloud, CloudClient, managed collections, Schema(), Search(), hybrid search, or Chroma Cloud CLI workflows.
Roblox Luau Development Workflow for implementing, reviewing, and refactoring Roblox game scripts, ModuleScript, ServerScript, LocalScript, RemoteEvent/RemoteFunction, DataStore, and server/client layering. Use when working on Roblox, Luau, Roblox Studio code, Rojo-synced scripts, gameplay systems, UI scripts, replication, remotes, or Roblox services.
Builds routing and origin-destination analysis workflows in CARTO. Triggers when the user mentions routing, route calculation, travel time, travel distance, OD matrix, origin-destination, isoline, isochrone, isodistance, catchment area, reachable area, drive time polygon, walk time polygon, service area, accessibility analysis, travel time matrix, distance matrix, commute patterns, trip flow, OD flow, mobility patterns, taxi trips, ride patterns, route geometry, shortest path, network distance, or wants to compute routes, generate isolines, build travel matrices, or analyze movement patterns between origins and destinations.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.
Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.