Browse skills

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.

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.

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.

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.

List, add, and update StyleSeed design tokens while keeping JSON sources, CSS variables, and dark-mode values in sync.

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.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.