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Found 67 Skills
LLM deployment strategies including vLLM, TGI, and cloud inference endpoints.
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
Generates a Jupyter notebook that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
Deploy and serve TensorFlow models
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents, manage RBAC permissions and role assignments, manage quotas and capacity, create Foundry resources. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring, create Foundry project, new Foundry project, set up Foundry, onboard to Foundry, provision Foundry infrastructure, create Foundry resource, create AI Services, multi-service resource, AIServices kind, register resource provider, enable Cognitive Services, setup AI Services account, create resource group for Foundry, RBAC, role assignment, managed identity, service principal, permissions, quota, capacity, TPM, deployment failure, QuotaExceeded. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app), generic Azure resource creation (use azure-create-app).
Build production-ready AI workflows using Firebase Genkit. Use when creating flows, tool-calling agents, RAG pipelines, multi-agent systems, or deploying AI to Firebase/Cloud Run. Supports TypeScript, Go, and Python with Gemini, OpenAI, Anthropic, Ollama, and Vertex AI plugins.
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Complete the full loop of "Data → Fine-tuning Training → Export → Deployment → Inference" using Bailian CLI (`bl`), or deploy base models directly without training. Supports fine-tuning of text models (SFT/DPO/CPT), audio TTS models (CosyVoice), and image generation models (Wan2.7). Covers dataset validation/upload, creating fine-tuning tasks, waiting for training completion, exporting the best checkpoint, creating inference deployments, waiting for readiness, and providing inference examples. This skill should be activated when users mention actions like "training models", "fine-tuning", "fine-tune", "finetune", "deploying models", "model launch", "running/calling fine-tuned models", "training an inference model", "continuing pre-training", "LoRA/SFT/DPO training", "speech synthesis models", "TTS fine-tuning", "CosyVoice", "voice cloning", "image generation fine-tuning", "text-to-image", "image-to-image", "Wan2.7", "image model training" on Bailian / DashScope / Alibaba Cloud Model Studio — even if users don't explicitly mention "using bl", as long as the intention is training or deployment on the Bailian platform, use this skill and do not assemble commands on your own.
Huawei Cloud Ascend model deployment and testing skill for large language models on Ascend DevServer (910B series). Supports single-machine and dual-machine deployment for LLM, VL (vision-language), Embedding, and Rerank models. Provides model inference testing, deployment log viewing, and status monitoring with automated model matching and deployment script generation. Use this skill when the user wants to: (1) deploy a model on Ascend DevServer, (2) test model inference, (3) view deployment logs or status, (4) list supported models, (5) check deployment prerequisites. Trigger: deploy, test, model list, deployment log, Ascend, DevServer, 910B, ModelArts, LLM, VL, Embedding, Rerank, multimodal, inference, model catalog, 昇腾, 部署模型, 测试模型, 模型列表, 部署日志, 模型部署, 推理测试
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Agent skill for neural-network - invoke with $agent-neural-network