Browse skills

Transcribe YouTube videos to text using OpenAI Whisper and yt-dlp. Use when the user wants to get a transcript from a YouTube video, generate subtitles, convert video speech to text, create SRT/VTT captions, or extract spoken content from YouTube URLs.

Download video and audio from YouTube and other platforms with yt-dlp. Use when a user asks to download YouTube videos, extract audio from videos, download playlists, get subtitles, download specific formats or qualities, batch download, archive channels, extract metadata, embed thumbnails, download from social media platforms (Twitter, Instagram, TikTok), or build media ingestion pipelines. Covers format selection, audio extraction, playlists, subtitles, metadata, and automation.

Validate data with Yup schemas. Use when adding form validation, defining API request schemas, validating configuration, or building type-safe validation pipelines in JavaScript/TypeScript.

Configure Zabbix for enterprise infrastructure monitoring with templates, triggers, discovery rules, and dashboards. Use when a user needs to set up Zabbix server, configure host monitoring, create custom templates, define trigger expressions, or automate host discovery and registration.

Automate workflows between apps with Zapier. Use when a user asks to connect apps without code, automate repetitive tasks, sync data between services, or build no-code integrations between SaaS tools.

Expert guidance for Zeabur, the cloud deployment platform that auto-detects frameworks, builds and deploys applications with zero configuration, and provides managed services like databases and message queues. Helps developers deploy full-stack applications with automatic scaling and one-click marketplace services.

Parallel read-only multi-agent code review of git diffs. Use when: reviewing diffs for regressions, security risks, performance issues, or wanting a parallel review swarm.

Expert guidance for Zed, the high-performance code editor built in Rust with native collaboration, AI integration, and GPU-accelerated rendering. Helps developers configure Zed, create custom extensions, set up collaborative editing sessions, and integrate AI assistants for productive coding.

Expert guidance for Zig, the systems programming language focused on performance, safety, and readability. Helps developers write high-performance code with compile-time evaluation, seamless C interop, no hidden control flow, and no garbage collector. Zig is used for game engines, operating systems, networking, and as a C/C++ replacement.

Deploy and configure Zipkin for distributed tracing and request flow visualization. Use when a user needs to set up trace collection, instrument Java/Spring or other services with Zipkin, analyze service dependencies, or configure storage backends for trace data.

You are an expert in Zod, the TypeScript-first schema declaration and validation library. You help developers define schemas that validate data at runtime AND infer TypeScript types at compile time — eliminating the need to write types and validators separately. Used for API input validation, form validation, environment variables, config files, and any data boundary.

Integrate and automate Zoho products. Use when a user asks to work with Zoho CRM, Zoho Books, Zoho Desk, Zoho Projects, Zoho Mail, or Zoho Creator, build custom integrations via Zoho APIs, automate workflows with Deluge scripting, sync data between Zoho apps and external systems, manage leads and deals, automate invoicing, build custom Zoho Creator apps, set up webhooks, or manage Zoho organization settings. Covers Zoho CRM, Books, Desk, Projects, Creator, and cross-product integrations.

You are an expert in Zustand, the small, fast, and scalable state management library for React. You help developers manage global state without boilerplate using Zustand's hook-based stores, selectors for performance, middleware (persist, devtools, immer), computed values, and async actions — replacing Redux complexity with a simple, un-opinionated API in under 1KB.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.