gary149

Create and deploy Gradio apps on HuggingFace Spaces. Covers full lifecycle: repo creation, ZeroGPU setup, custom theming/CSS, model loading, API integration, video generation, persistent storage with HF Buckets, and deployment. Use when asked to: build a Gradio app, create an HF Space, deploy a demo, make an interactive ML demo, build a Space with GPU, style a Gradio app, add persistent storage to a Space, mount a bucket, or any task involving Gradio + HuggingFace Spaces. Triggers on: "gradio", "hf space", "huggingface space", "build a space", "create a demo", "deploy on spaces", "zerogpu", "gradio app", "persistent storage", "mount bucket", "space storage".

End-to-end workflow to create a custom Krea 2 image LoRA: research and assemble a training dataset (source images, curate by vision, write captions, optimize resolution), train it on fal's krea-2-trainer, validate the result, and publish it to the Hugging Face Hub with a proper model card and native <Gallery />. Use this whenever the user wants to train a Krea 2 LoRA, make a custom Krea image style/character/object, "train a lora like the krea ones", build or caption a dataset for Krea 2, fine-tune Krea 2 RAW/Turbo, run the fal krea-2-trainer, or publish a Krea LoRA to Hugging Face — even if they mention only one phase (e.g. "caption these images for a krea lora", "what resolution for krea training", or "publish my krea lora"). Also triggers on artist-style LoRAs for Krea (e.g. a Magritte or Ghibli style) and on questions about Krea LoRA steps / learning rate / dataset size.

Create and deploy Gradio apps on HuggingFace Spaces. Covers full lifecycle: repo creation, ZeroGPU setup, custom theming/CSS, model loading, API integration, video generation, and deployment. Use when asked to: build a Gradio app, create an HF Space, deploy a demo, make an interactive ML demo, build a Space with GPU, style a Gradio app, or any task involving Gradio + HuggingFace Spaces. Triggers on: "gradio", "hf space", "huggingface space", "build a space", "create a demo", "deploy on spaces", "zerogpu", "gradio app".

Monitor and report on active pi agent training sessions. Use when asked to "track a pi session", "report on training", "what is pi doing", "follow the training", "monitor the experiment loop", or to check on a running autotrain/autoresearch session.

Invoke the pi coding agent CLI as a sub-agent. Use when delegating work to pi, running pi programmatically, sending prompts to a specific LLM model via pi, or when users say "use pi", "run pi", "ask pi", "pi agent", "delegate to pi". Includes orchestration best practices for context passing, tool scoping, and multi-agent workflows.

Generate images, videos, audio, and more using HuggingFace Spaces and Inference Providers directly. Supports batch generation (e.g. "generate 10 images"), chaining multiple Spaces, and finding the right Space for any task. Use when asked to: generate images, create videos, text-to-speech, batch generate content, use a Gradio Space, call HF models, or any AI generation task that doesn't involve building a daggr pipeline. Triggers on: "generate images", "create a video", "text to speech", "use this Space", "batch generate", "generate 10 images", "image generation", "video generation".

Write changelog entries for Hugging Face Hub features. Use when asked to write a changelog, create a changelog entry, or document a new feature/PR for hf.co/changelog. Triggers on "write changelog", "changelog entry", "document this PR/feature for changelog".

Expert knowledge for the Hugging Face Hub ecosystem including HTTP/curl API usage, Python/JavaScript SDKs, Spaces apps (Gradio, Docker), model formats (GGUF, PEFT/LoRA), datasets (pandas, Polars, DuckDB), automation (webhooks, Jobs), and security features. Use when working with Hub APIs, huggingface_hub Python code, Spaces apps, inference providers, model/dataset repositories, or Hub automation.

Add a new AI image model to the VM Studio comparison system. Use when the user wants to add a new model to the comparison pages, include a model in comparisons, or update the model comparison system. Triggers on requests like "add [model] to compare", "include [model] in comparisons", "add new model to comparison pages", or "update compare models with [model]". Handles the complete workflow including API research, image generation, quality evaluation, data updates, and page regeneration.

Complete SDK for controlling Reachy Mini robot - head movement, antennas, camera, audio, motion recording/playback. Covers architecture (daemon/client), deployment modes (USB, wireless, simulation, on-Pi), and app distribution. Also includes advanced application patterns: MovementManager, layered motion, audio-reactive movement, face tracking, LLM tool systems, and OpenAI realtime integration. Use when: (1) Writing code to control Reachy Mini, (2) Moving the robot head or antennas, (3) Accessing camera/video, (4) Playing/recording audio, (5) Recording or playing back motions, (6) Looking at points in image or world space, (7) Understanding robot capabilities, (8) Connecting to real or simulated robot, (9) Building conversational AI apps, (10) Integrating with LLMs/OpenAI, (11) Deploying apps to robot, (12) Any robotics task with Reachy Mini.