create-component
Create a new React Native/Expo component with guided workflow following compound component API pattern
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Create a new React Native/Expo component with guided workflow following compound component API pattern
Create or modify gluestack-core and gluestack-utils packages for component creators and utilities
Improve or extend an existing gluestack-ui component with new features, variants, or fixes
Enforces constrained, opinionated styling patterns for gluestack-ui v4. Main overview skill that coordinates specialized sub-skills for setup, components, styling, variants, performance, and validation.
Guide for installing gluestack-ui v4 per the official Installation doc - CLI and Manual paths only. Follow https://v4.gluestack.io/ui/docs/home/getting-started/installation strictly.
Transform an AI agent into a tasteful, disciplined development partner. Not just a code generator, but a collaborator with professional standards, transparent decision-making, and craftsmanship. Use for any development task: building features, fixing bugs, designing systems, refactoring. The human provides vision and decisions. The agent provides execution with taste and discipline.
Add support for a HuggingFace model in XTuner so it can be trained with the full set of parallelism strategies and optimization switches. The user can provide a HuggingFace Hub repo id, a local model directory, or a model family already supported by `transformers`. This skill walks through the full path: locating the reference implementation, classifying the model, splitting new code across the model / module / ops layers, implementing the XTuner model class and config, registering the entry point, validating bitwise numerical parity with HuggingFace, writing regression tests, and enabling the training optimizations (EP/SP, micro-batch, torch.compile, fp8, activation offload).
Normalize an HF model checkpoint — quantize bf16/fp16 weights to per-block FP8, and/or repack engine-named safetensors into HF-standard ~4GB shards.
Merge a base HF model dir with an extra HF model dir by index diff — take tensors only present in extra, append them to a new output dir in HF-standard shard layout. Base wins on overlap.