ML Ops
Machine learning operations
event-modeling
by jwilger
Event modeling facilitation for discovering and designing event-sourced systems. Four phases: domain discovery, workflow design (9-step process), GWT scenario generation, and model validation. Activate when starting a new project, designing features, modeling domains, writing Given/When/Then scenarios, or discussing event sourcing and domain-driven design.
pipeline
by jwilger
Autonomous build-phase orchestrator. Manages slice queue, TDD pair dispatch, full-team code review, mutation testing, CI integration, and auto-merge with quality gates. Replaces manual coordinator overhead during build phase. Activate when running factory mode with ensemble-team.
hugging-face-evaluation-manager
by Nymbo
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
dbt-expert
by timequity
dbt best practices for models, tests, documentation, and project organization.
dspy-prompting
by eyadsibai
Use when "DSPy", "declarative prompting", "automatic prompt optimization", "Stanford NLP", or asking about "optimizing prompts", "prompt compilation", "modular LLM programming", "chain of thought", "few-shot learning"
nano-banana
by Nymbo
Generate and edit images using the Gemini API (Nano Banana). Use this skill when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
gemini-api
by diskd-ai
Google Gemini API integration for building AI-powered applications. Use when working with Google's Gemini API, Python SDK (google-genai), TypeScript SDK (@google/genai), multimodal inputs (image, video, audio, PDF), thinking/reasoning features, streaming responses, structured outputs with JSON schemas, multi-turn chat, system instructions, image generation (Nano Banana), video generation (Veo), music generation (Lyria), embeddings, document/PDF processing, or any Gemini API integration task. Triggers on mentions of Gemini, Gemini 3, Gemini 2.5, Google AI, Nano Banana, Veo, Lyria, google-genai, or @google/genai SDK usage.
shap
by eyadsibai
Use when "SHAP", "Shapley values", "feature importance", "model explainability", or asking about "explain predictions", "interpretable ML", "feature attribution", "waterfall plot", "beeswarm plot", "model debugging"
experiment-tracking
by eyadsibai
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
llm-training
by eyadsibai
Use when "training LLM", "finetuning", "RLHF", "distributed training", "DeepSpeed", "Accelerate", "PyTorch Lightning", "Ray Train", "TRL", "Unsloth", "LoRA training", "flash attention", "gradient checkpointing"
scikit-learn
by eyadsibai
Use when "scikit-learn", "sklearn", "machine learning", "classification", "regression", "clustering", or asking about "train test split", "cross validation", "hyperparameter tuning", "ML pipeline", "random forest", "SVM", "preprocessing"
pymc
by eyadsibai
Use when "PyMC", "Bayesian", "MCMC", "probabilistic programming", or asking about "Bayesian regression", "hierarchical model", "NUTS sampler", "posterior distribution", "prior predictive", "credible intervals", "uncertainty quantification"
multimodal-models
by eyadsibai
Use when "CLIP", "Whisper", "Stable Diffusion", "SDXL", "speech-to-text", "text-to-image", "image generation", "transcription", "zero-shot classification", "image-text similarity", "inpainting", "ControlNet"
context-fundamentals
by Nymbo
Understand the components, mechanics, and constraints of context in agent systems. Use when designing agent architectures, debugging context-related failures, or optimizing context usage.
deepagents-overview
by christian-bromann
Understanding Deep Agents framework - what they are, how to create them with createDeepAgent, and the agent harness architecture with built-in middleware for planning, filesystems, and subagents.
flash-attention
by tylertitsworth
"Flash Attention, FlashInfer, SDPA backends, PagedAttention, and attention kernel selection/configuration. Use when choosing or configuring attention backends for training or inference (FlashAttention-2/3, FlashInfer, SDPA, xFormers, PagedAttention, Ring Attention, FlexAttention/FlexDecoding, varlen_attn)."
transformers-js
by nico-martin
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in Node.js and browsers (with WebGPU/WASM) using pre-trained models from Hugging Face Hub.
understanding-db-schema
by C0ntr0lledCha0s
Deep expertise in Logseq's Datascript database schema. Auto-invokes when users ask about Logseq DB schema, Datascript attributes, built-in classes, property types, entity relationships, schema validation, or the node/block/page data model. Provides authoritative knowledge of the DB graph architecture.
azure-ai-formrecognizer-java
by Tryboy869
"Build document analysis applications with Azure Document Intelligence (Form Recognizer) SDK for Java. Use when extracting text, tables, key-value pairs from documents, receipts, invoices, or building custom document models."
currying-inference
by marius-townhouse
Use when generic types aren't inferred. Use when builder patterns need better types. Use when creating new inference sites.
aiconfigurator
by tylertitsworth
"NVIDIA AIConfigurator — optimal LLM serving configuration for disaggregated/aggregated deployments, parallelism selection (TP/PP/EP/DP), quantization, and MOE planning. Use when planning model deployment topology on NVIDIA GPUs."
world-labs-text-prompt
by CloudAI-X
Text-to-world generation best practices, prompt structure, style descriptors
building-commands
by C0ntr0lledCha0s
Expert at creating and modifying Claude Code slash commands. Auto-invokes when creating/updating commands, modifying command frontmatter (model, allowed-tools, argument-hint), designing workflows, or writing to /commands/.md files.
data-product-thinking
by hollandkevint
First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"