majiayu000/claude-skill-registry - Page 35

model-convergence-forecast

Forecast convergence patterns in multi-model consensus scenarios.

model-evaluation-benchmark

Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.

my-ml-project

Managing ML experiments, metrics, parameters, and artifacts using MLflow, Weights & Biases, and best practices for reproducible ML experiments and model versioning.

explainability

See the main Model Explainability skill for comprehensive XAI coverage.

model-explainer

Model interpretability and explainability using SHAP, LIME, feature importance, and partial dependence plots. Activates for "explain model", "model interpretability", "SHAP", "LIME", "feature importance", "why prediction", "model explanation". Generates…

model-interpretability

Interpret ML model predictions using SHAP, LIME, attention visualization, and probing techniques. Use when explaining model decisions, debugging model behavior, or building trust in ML systems.

model-optimization

Comprehensive guide for ML model optimization techniques including quantization, pruning, knowledge distillation, and inference optimization.

ai-model-registry

Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.

model-registry

Centralized model versioning, staging, and lifecycle management. Activates for "model registry", "model versioning", "model staging", "deploy to production", "rollback model", "model metadata", "model lineage", "promote model", "model catalog". Manages ML…

model-risk-management

Model Risk Management (MRM) is a framework designed to manage the risk of adverse consequences resulting from decisions based on incorrect or misused model outputs. While software engineering focuses

model-serving-inference

Model serving is the process of deploying ML models to production and handling inference requests efficiently at scale.

model-serving-kubernetes

Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.

model-training

Model training is the process of teaching machine learning models to make predictions or decisions based on data. This skill covers comprehensive training workflows including pipeline design, data pre

model-versioning

Model versioning is practice of tracking and managing different versions of machine learning models throughout their lifecycle. This skill covers versioning strategies, model registries, metadata mana

multimodal-medical-imaging

Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports.

multimodal-ml

Vision-language model patterns including CLIP, LLaVA, cross-modal alignment, and embedding fusion. Use when building or integrating multimodal ML systems.

multimodal-rag

CLIP, SigLIP 2, Voyage multimodal-3 patterns for image+text retrieval, cross-modal search, and multimodal document chunking. Use when building RAG with images, implementing visual search, or hybrid retrieval.

musik-app-bei

Datenbankrecht für Musik-, Film- und Bildarchive: §§ 87a-87e UrhG für Mediendatenbanken, Schichtenschutz (Datenbankherstellerrecht + Urheberrecht an Einzelwerken), Lizenzmodelle für Stock-Media-Portale und Verwertungsgesellschaften (GEMA, VGBild),…

Source text: German

nano-banana-pro

Generate or edit images via Gemini 3 Pro Image (Nano Banana Pro).

nano-banana-pro

Generate or edit images via Gemini 3 Pro Image (Nano Banana Pro). RoastPlusでの用途: コーヒー豆の視覚化、焙煎度合いの比較画像、クイズ用の画像生成、設定画面のアイコン作成等。

Source text: Mixed languages

nano-banana-pro

Gere ou edite imagens via Gemini 3 Pro Image (Nano Banana Pro).

nano-banana-pro

Generate or edit images via Gemini 3 Pro Image (Nano Banana Pro).

nanobanana

StudioJinsei用Nanobanana画像生成Skill。Google Gemini APIを使用してロゴ、コトネちゃん、サイトビジュアル等を生成します。

Source text: Japanese

gemini-image-generator

Generate images using Google Gemini NanoBanana via browser automation. Use this skill for general-purpose AI image generation from text prompts. Includes persistent authentication, automatic environment setup, and reference image support for style matching.

nanobanana-prompts

NanoBanana（Google Gemini画像生成）向けの高品質プロンプトを生成。画像生成、編集、フェイススワップ、背景変更、キャラクター一貫性のプロンプト作成を支援。

Source text: Japanese

neuropixels-analysis

Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when…

new-sdg

Implement a new synthetic data generator using NeMo Data Designer by defining its configuration and executing a preview job.

nixtla-anomaly-detector

Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Trigger with "detect anomalies", "find outliers",…

nixtla-batch-forecaster

Forecast multiple time series in parallel using TimeGPT. Use when processing 10-100+ contracts efficiently. Trigger with 'batch forecast' or 'parallel forecasting'.

nixtla-cross-validator

Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with cross validate time series, evaluate forecasting model, time series backtesting.

nixtla-liquidity-forecaster

Forecasts orderbook depth and spreads to optimize trade execution timing. Use when needing to estimate market liquidity for large orders. Trigger with 'forecast liquidity', 'predict orderbook', 'estimate depth'.

nixtla-model-selector

Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with 'auto-select model', 'choose best model', 'model selection'.

nixtla-timegpt-finetune-lab

Configure TimeGPT fine-tuning on custom datasets with Nixtla SDK. Use when training domain-specific forecast models. Trigger with 'fine-tune TimeGPT' or 'train custom model'.

nixtla-timegpt-lab

Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Activates when user needs forecasting, time series…

nlp-pipeline-builder

Natural language processing ML pipelines for text classification, NER, sentiment analysis, text generation, and embeddings. Activates for "nlp", "text classification", "sentiment analysis", "named entity recognition", "BERT", "transformers", "text…

nlp-processing

Process and analyze natural language using modern NLP techniques. Use for text classification, named entity recognition, sentiment analysis, tokenization, embeddings, transformers (BERT, GPT), and language understanding tasks.

nnsight-basics

Core nnsight concepts for neural network interpretability. Use when setting up models, tracing activations, saving values, or making basic interventions on model internals.

bio-metabolomics-normalization-qc

Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.

numpy-core

Fundamental NumPy operations including ndarray creation, dtypes, shape manipulation, and basic operations with a focus on memory alignment and data views. Triggers: numpy, ndarray, dtype, reshape, memory alignment, array-creation.

numpy-fft

Discrete Fourier Transform routines for spectral analysis, signal filtering, and frequency-domain operations. Triggers: fft, fourier transform, spectral analysis, rfft, fftshift, ifft.