Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots. — from ilderaj/agent-plugin-marketplace
Use when when you have: (1) a collection of molecules represented as molecular graphs (nodes=atoms, edges=bonds with chirality/order attributes); (2) structured metadata…
Use when you have a baseline GNN model trained on a molecular property prediction task (e.
Use when evaluating GraphRAG system quality across knowledge graph completeness, retrieval relevance, answer correctness, and reasoning verification.
Use when designing complete GraphRAG systems that combine graph retrieval with LLM reasoning. Invoke when user mentions GraphRAG system, technology stack, Neo4j with LLM,…
Hybrid interview that probes AI-engineering mastery by tip-vocabulary depth — entity referencing, loop closure, observability, harness improvement — not by token usage or LOC.
Hierarchical Task Network planning (provably correct plans via symbolic decomposition + LLM fallback) and AlphaEvolve evolutionary code search (fitness-gated genetic algorithm).
Manage Huawei ModelArts training jobs (GPU and Ascend NPU cost governance), Pangu foundation model deployment, AI Gallery model management, and MLOps pipeline automation for AI/ML…
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots. — from yanochka11/harness_bro
Deploys models from Hugging Face Hub to Inference Endpoints using the huggingface_hub client and REST API.
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference…
Receive and verify Hugging Face webhooks. Use when setting up Hugging Face webhook handlers, debugging X-Webhook-Secret verification, or handling events on models, datasets, and…
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or…
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral).
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, o — from…
Design, generate, and fully deploy a stylish, production-ready Retrieval-Augmented Generation (RAG) chatbot embedded directly into any website/project.
Build the internal link graph for a site, run PageRank-style authority distribution, detect orphan pages, and recommend new internal links via embedding-based semantic similarity…
Use when you have defined a Keras model architecture (convolutional and dense layers) accepting raw mass spectrometry imaging data tensors and need to prepare it for training on…
KV-cache optimization patterns for LLM inference. Prefix caching, sliding window attention, cache reuse across turns, static cache for fixed prompts, and TTFT reduction…
Use when implementing a LangChain-based agent runtime from an approved ai-architecture.md agent control-flow design.
LightRAG is a Python-based retrieval-augmented generation framework that builds knowledge graphs from documents for more connected, contextual retrieval.
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral).
Automatically applies when building LLM applications. Ensures proper async patterns for LLM calls, streaming responses, token management, retry logic, and error handling.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Use when building LLM applications: prompt engineering, structured output, agents, RAG integration, memory management, or production deployment.
Build automated LLM evaluation pipelines with benchmarks, regression tests, RAGAS, and human eval workflows.
LLM Fine-Tuning expert. Covers LoRA, QLoRA, PEFT, dataset preparation, Hugging Face Trainer/TRL, RLHF, DPO, quantization (GPTQ/AWQ/GGUF), model merging, distributed training with…
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems.
Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from…
MemGPT virtual context — OS virtual-memory analogy for LLM context management. Two-tier (main context = RAM, external store = disk), page-in/page-out tools, archival/core memory…
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Operate as an ML engineer who takes a research model to reliable production behind eval gates and keeps it healthy across its lifecycle.
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring. — from belokonm/claude-supercode-skills
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring. — from majiayu000/claude-skill-registry
Plan and execute production ML engineering work — model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, and…
Déploiement de modèles ML en production (MLOps). Se déclenche avec "déployer un modèle", "ML deployment", "MLOps", "model serving", "inference", "model registry", "ML pip — from…
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates.
Orchestrates complete machine learning pipelines within SpecWeave increments. Activates when users request "ML pipeline", "train model", "build ML system", "end-to-end ML", "ML…
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. — from whatamelon/AGENT-HARNESS-BASELINE
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. — from majiayu000/claude-skill-registry
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and…
Implement advanced MLOps practices for production ML systems. Use for: building CI/CD pipelines for ML models, implementing continuous training and monitoring, managing model…
Design DAG-based MLOps pipeline architectures with Airflow, Dagster, Kubeflow, or Prefect. Activates for DAG orchestration, workflow automation, pipeline design patterns, CI/CD…
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving.
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving.
Fine-tune LLMs and ML models — LoRA, QLoRA, PEFT, Hugging Face. Dataset prep, training, evaluation, deployment
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
Model Registry Manager - Auto-activating skill for ML Deployment. Triggers on: model registry manager, model registry manager Part of the ML Deployment skill category.
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised…
Deploy ML models as production APIs with vLLM, TGI, ONNX Runtime, batching, autoscaling, and GPU optimization.