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evaluation
LLM evaluation and prompt optimization with Evidently.ai. Covers text descriptors, dataset metrics, LLM-as-a-Judge patterns, and automated prompt optimization for classification and generation tasks.
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tools machine learning AI Skills
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reward
Reward model training for RLHF pipelines. Covers RewardTrainer, preference dataset preparation, sequence classification heads, and reward scaling for stable reinforcement learning. Includes thinking quality sco...
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vision
Vision model fine-tuning with FastVisionModel. Covers Pixtral, Ministral VL training, UnslothVisionDataCollator, image+text datasets, and vision-specific LoRA configuration.
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qlora
Advanced QLoRA experiments and comparisons. Covers alpha scaling, LoRA rank selection, target module strategies, continual learning, multi-adapter hot-swapping, and quantization comparison (4-bit vs BF16).
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backend--models
Apply the Agent OS standard for backend models.
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ralph-autonomous
Autonomous coding patterns for unattended feature implementation using Ralph loop runner. Use when running Ralph autonomous loop, implementing features without supervision, handling long-running agent sessions,...
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GitHub
llm-knowledge
This skill should be used when the user asks "what is LoRA", "compare models", "which model is best for Chinese", "SFT vs DPO", "how to handle overfitting", "class imbalance solution", "model architecture", "tr...
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GitHub
writing-plans
This skill should be used when the user asks to "write a plan", "create training plan", "plan my fine-tuning", "make a plan for model training", or when starting a new training task that requires multiple steps...
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GitHub
llm-coach
This skill should be used when the user asks to "train a model", "fine-tune", "build NLP model", "create training task", "optimize model performance", "improve accuracy", "what model should I use", or expresses...
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GitHub
finetune-llm
LLM fine-tuning 教練式引導工作流程 v2。 核心功能：主動探索使用者痛點、引導明確目標、多任務管理、資料來源追蹤、完整版本 lineage。 支援：LoRA/QLoRA/DoRA 微調、SFT/ORPO/DPO 對齊、資料準備、Benchmark 評估、HuggingF...
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GitHub
token-efficiency
Token optimization best practices for cost-effective Claude Code usage. Automatically applies efficient file reading, command execution, and output handling strategies. Includes model selection guidance (Opus f...
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GitHub
compound
Compound engineering workflow for self-improvement. Auto-loads when completing features, doing reviews, or capturing learnings into CLAUDE.md and rules.
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GitHub
agent-mlops
Production deployment and operationalization of AI agents on Databricks. Use when deploying agents to Model Serving, setting up MLflow logging and tracing for agents, implementing Agent Evaluation frameworks, m...
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GitHub
mosaic-ai-agent
Expert guidance for building production-ready tool-calling agents with Databricks Mosaic AI Agent Framework. Use when users need to create agents that orchestrate multiple data sources or APIs, implement LangCh...
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GitHub
bugs-fundamentals
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
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GitHub
time-series-models
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
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GitHub
stan-fundamentals
Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
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GitHub
survival-models
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
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regression-models
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
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GitHub
pymc-fundamentals
Foundational knowledge for writing PyMC 5 models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
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GitHub
cv-strategy
Cross-validation configuration and fold management for this competition
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GitHub
google-ai-studio
Enables Claude to use Google AI Studio for testing prompts, exploring models, and prototyping AI applications
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GitHub
deploy
Deploy applications to AWS (SageMaker, Amplify, EC2). Use this skill to deploy models, frontends, or manage infrastructure. Invoke with /deploy.
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GitHub
vlm-embed
Manage VLM (Vision-Language Model) embedding service and jobs. Use this skill to start the Flask embedding service, run embedding jobs, or train the AE3D autoencoder. Invoke with /vlm-embed.
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