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Found 38 Skills
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.
阿里云百炼模型精调训练入口：用户要精调、微调、训练自己的模型（fine-tune，支持 SFT / SFT-LoRA / DPO / DPO-LoRA / CPT， 覆盖文本、语音、图像）、校验或上传训练数据集、看训练进度和日志、挑 checkpoint、导出精调产物、 把专属模型部署成服务时使用 `bl dataset` / `bl finetune` / `bl deploy`。链路是 validate 校验数据 → upload 拿 file-id → finetune create 建任务 → watch 看进度 → export 导出 → deploy 上线，需要 API key； 写操作先用 `--dry-run` 预览。反触发：用户点名火山方舟/ark 的精调不走本 skill；只是要选哪个模型走 bailian-model-recommend；用现成模型生图生视频走 bailian-gen；百炼其他资源管理走 bailian-cli。 官方安装：`npx skills add modelstudioai/cli --all -g`（与共享协议 bailian-protocol 同装）。
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.
Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.
Large Language Model development, training, fine-tuning, and deployment best practices.
Agent skill for neural-network - invoke with $agent-neural-network
Agent skill for data-ml-model - invoke with $agent-data-ml-model
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure. Designs scalable large language model applications with focus on performance, cost efficiency, and safety.