Hugging Face plugins

Perform the full ML lifecycle on Hugging Face Hub: create and manage datasets, find and evaluate models, fine-tune with TRL or Unsloth, build and deploy Gradio demos on Spaces, log training metrics, and publish research papers — all via CLI or agent skills.

Build recommendation engines by generating Python code for collaborative, content-based, or hybrid filtering using scikit-learn, TensorFlow, or PyTorch to personalize movies, products, or content. Analyze context to produce complete AI/ML tasks with validation, error handling, performance metrics, insights, artifacts, and documentation.

Optimize Python deep learning models using Adam, SGD optimizers, learning rate schedulers, and regularization to improve accuracy and reduce training time. Generate production-ready AI/ML code from context analysis, including validation, error handling, performance metrics, insights, artifacts, and documentation.

Explain machine learning model predictions using SHAP, LIME, and feature importance to identify influential features and debug behavior. Generate production-ready AI/ML code from context, including validation, error handling, performance metrics, insights, artifacts, and documentation.

Fine-tune pre-trained ML models like ResNet, BERT, and GPT on custom datasets using transfer learning. Generates production-ready Python code with validation, error handling, performance metrics, documentation, and saves artifacts for deployment.

Deploy, debug, optimize, monitor, and secure GPU-accelerated ML inference and training workloads on CoreWeave Kubernetes clusters, including cost tuning, data handling, migrations from AWS/GCP/Azure, CI/CD automation, and production checklists.

Evaluate single ML models or compare multiple ones on test datasets across classification, regression, NLP, and generative tasks. Compute metrics, statistical significance, inference performance, costs, robustness, bias checks; generate visualized reports with confusion matrices, performance profiles, tables, rankings, and recommendations.

Rapidly implement production-ready AI/ML features in apps: integrate LLMs with prompt engineering and response handling, build ML pipelines for recommendation systems, add computer vision for visual search, and enable intelligent automation using OpenAI, Anthropic, LangChain, Hugging Face, or Ollama.

Store, search, and recall persistent cross-session memories for Claude Code using Ebbinghaus decay, enabling the AI to retain user facts, preferences, and project decisions across conversations.

Rapidly implement production-ready AI/ML features in apps: integrate LLMs via prompt engineering and response handling, build ML pipelines for user behavior-based recommendations, add computer vision for photo-based product search, and deploy intelligent automations.

Delegate image analysis, OCR text extraction, barcode/QR detection, and document processing to a vision expert agent using latest models like GPT-4V, Claude Vision, Mistral-OCR, Tesseract, and EasyOCR for efficient visual AI workflows.

Rapidly implement production-ready AI/ML features in apps, including LLM integrations with prompt engineering, ML pipelines for recommendations, computer vision for visual search, and intelligent automation, using a specialized agent.

Build RAG pipelines for document Q&A and chatbots by chunking large docs, generating embeddings, storing in vector DBs, and retrieving context to reduce hallucinations. Engineer and optimize LLM prompts using chain-of-thought, few-shot examples, constitutional AI, meta-prompting, and validation workflows.

Delegate advanced image analysis workflows to expert vision AI subagents that perform OCR with Tesseract/EasyOCR, barcode/QR detection, document processing, and optimization using cutting-edge models like GPT-4V, Claude Vision, and Mistral-OCR.

Enforce architectural principles with feature-layer project knowledge as a navigable tree, scaffold new layers and feature narratives, and orchestrate multi-agent code reviews, pixel-art quality audits, and long-running AI workflows with safety hooks and session recovery.

Automates the full lifecycle of migrating and optimizing AI models for Huawei Ascend NPUs: environment setup, code analysis and adaptation, operator development (AscendC/Triton), distributed training with MindSpeed/Megatron, performance profiling and tuning, precision verification, and deployment as vLLM inference services.

Delegate end-to-end ML engineering workflows to specialized agents that construct data preparation and training pipelines with feature engineering and hyperparameter tuning, optimize inference through quantization, pruning, batching, and edge deployment, and manage MLOps for model versioning, monitoring, A/B testing, and production orchestration.

Diagnose and fix ML training failures (OOM, NaN, divergence), generate citation-grounded implementation plans for fine-tuning and inference pipelines, and verify code/configs against official framework docs before running GPU jobs.

Consult a virtual CTO team of specialized agents to design scalable architectures, generate phased roadmaps with effort estimates, recommend tech stacks, validate plans with ruthless reports identifying flaws and risks, challenge assumptions, estimate costs, and decide build-vs-buy for web, mobile, or AI/ML projects.

Deploy vLLM OpenAI-compatible inference servers locally with hardware detection, via Docker images, or Kubernetes YAML manifests with GPU support, then benchmark throughput, TTFT, TPOT, inter-token latency, and prefix caching using synthetic data, ShareGPT, or fixed prompts.

Run GGUF models locally with Mozilla Llamafile, launching OpenAI-compatible API servers configurable for GPU/CPU inference, SDK integrations, installations, startups, and connection troubleshooting in offline setups.

Find, compare, run, and prompt AI models hosted on Replicate directly from the editor, including building custom models with Cog, searching by task, and deploying via CI/CD

Automate image processing, OCR, barcode/QR detection, and document analysis with expert vision AI. Proactively select optimal models like GPT-4V, Claude Vision, or Mistral-OCR; engineer prompts, preprocess images, benchmark performance, and integrate APIs from OpenAI, Anthropic, and Hugging Face.

Generate and edit images and videos inside Claude Code using ComfyUI — build, visualize, and debug Stable Diffusion and Flux workflows, run parameter sweeps, diagnose failures, and orchestrate multi-step pipelines like portrait editing or story-to-video.

Build high-quality computer vision datasets and models with FiftyOne: import/export datasets, run inference, evaluate predictions, find duplicates, visualize embeddings, and develop custom plugins — all from within Claude.

Routes OpenAI-compatible clients through FreeRide, a local gateway that distributes inference requests across free-tier AI providers (OpenRouter, Groq, NVIDIA NIM, Cloudflare Workers AI, HuggingFace). Detects a running FreeRide instance and wires any OpenAI-shaped client against it for cost-free model access.

Automate end-to-end ML performance investigations: research SOTA papers and architectures, generate phased plans, judge experimental methodologies, profile bottlenecks, run metric-improvement campaigns with atomic git commits, auto-rollback on regressions, and leverage specialist agents for data lifecycle and deep paper analysis.

Build full-stack dApps on Ritual Chain using skills and agents that generate Solidity smart contracts with async precompiles for on-chain ML inference, HTTP/LLM calls, scheduling, secrets, X402 payments; create React/Next.js frontends with wagmi hooks; set up backends, testing, debugging, and deployment workflows from idea to verification.

Enforce a falsification-driven research pipeline for empirical ML and evaluation work—transform vague ideas into preregistered hypotheses, reproduce baselines, run adversarial attacks, apply statistical rigor, and gate decisions on kill-or-ship based on repository evidence.

Build production-grade LLM apps in Python: implement RAG pipelines with embeddings and hybrid search, design LangChain/LangGraph agents, optimize prompts, tune vector indexes, and evaluate performance using AI agents, skills, and commands for architecture, code gen, and benchmarking.

Transcribe handwritten historical Swedish documents using HTR tools with interactive viewers and JSON exports, search Riksarkivet archives via metadata and transcriptions, browse and zoom PDFs/IIIF images, annotate pages in Label Studio, map topics to record types, and upload files to Hugging Face Spaces for remote access.

Index your local PDFs, markdown files, and source code into a searchable dual-index (Qdrant for semantic vector search, MeiliSearch for full-text keyword search) via the arc CLI, then query across corpora with conceptual or exact-term lookups — including agent memory persistence.

Reproduce Long Video Sparse Attention (LVSA) paper headline numbers using bundled benchmarks scripts, including SotA comparison, latency scaling, and scoring with VQeval and VBench-Long, plus figure regeneration.

Build dynamic neural networks that grow, prune, and adapt topology during training in Python with Hugging Face. Design state machines for module lifecycles, diagnose issues in growth decisions, gradient isolation, and integration, plus consult an expert advisor for continual learning, PEFT/LoRA, and modular composition.

Delegate expert-level AI/ML workflows to specialized agents: engineer optimized prompts with evaluation and A/B testing, architect scalable LLM systems with RAG/LoRA fine-tuning, build production NLP pipelines for NER/classification/QA, and deploy optimized models via vLLM/Triton/Docker/K8s for reliability, performance, and cost control.

Train and run inference on machine learning models using Hugging Face Transformers and PEFT with PyTorch on cloud GPUs from Modal, Lambda Labs, or RunPod—no local GPU required.

Discover, evaluate (quality, licensing, provenance), and acquire datasets from Kaggle, HuggingFace, IPFS, arXiv, DBLP for AI model training and fine-tuning. Download free or paid data via local stdio MCP server—no API keys needed.

Build, configure, test, and debug dataflow pipelines for the dora-rs robotics framework — covering YAML wiring, Rust/Python node development, ML/vision processing, audio pipelines, robot control, data recording, and simulation with Rerun visualization