Total 55,107 skills, AI & Machine Learning has 9147 skills
Showing 12 of 9147 skills
Publish a Harbor task or dataset to the registry. Use when the user wants to upload, publish, or share tasks or datasets/benchmarks on the Harbor registry.
Calculate MFU (Machine FLOP Utilization) for operators such as matmul/GEMM, and provide clear formulas and derivation processes.
Full production pipeline — story to scenes, Z-Image start frames, Qwen Edit end frames, WAN FLF video clips, ffmpeg concatenation
Use when executing implementation plans. Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.
Behavioral guardrails for Cavekit agents. Four principles — think before coding, simplicity first, surgical changes, goal-driven execution — that prevent over-engineering, silent assumptions, scope creep, and unfocused work. Every task-builder, reviewer, planner, and inspector must internalize these before writing a single line. Trigger phrases: "guardrails", "karpathy", "scope creep", "over-engineering", "stop adding features", "surgical fix".
Google Gemini integration. Manage Users, Conversations. Use when the user wants to interact with Google Gemini data.
Command-line interface for ComfyUI - AI image generation workflow management via ComfyUI REST API. Designed for AI agents and power users who need to queue workflows, manage models, download generated images, and monitor the generation queue without a GUI.
Give an AI agent an encrypted inbox with the masumi-agent-messenger CLI. Use when agents need to message other agents, read durable inboxes, manage threads, coordinate async multi-agent workflows, request human approval, or automate inbox operations with JSON output.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch for Claude Code or Cursor, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Comprehensive guide to the AgentMail Python and TypeScript SDKs. Use when building AI agents that need their own email inboxes, sending or receiving emails programmatically, managing threads and conversations, handling attachments, creating drafts for human-in-the-loop approval, setting up real-time notifications via webhooks or WebSockets, configuring custom domains, managing allow/block lists, using pods for multi-tenant isolation, or integrating email into any AI agent workflow. Covers the full AgentMail API with code examples, best practices, and production patterns.
Deploy vLLM using Docker (pre-built images or build-from-source) with NVIDIA GPU support and run the OpenAI-compatible server.
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.