majiayu000/claude-skill-registry - Page 32

dynamic-sufficiency

Causal state gating via ε-machine. Coworld observer that prevents action

east-py-datascience

Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy), machine learning (XGBoost, LightGBM, NGBoost, Torch MLP, Lightning, GP), ML…

edge-and-mobile-ml

Use when deploying ML models to edge devices, mobile, or browser. Covers ONNX export, CoreML conversion, TensorRT optimization, quantization (PTQ/QAT), and model profiling.

edge-model-compression

Edge Model Compression enables deployment of large, accurate machine learning models on resource-constrained edge devices through techniques like quantization, pruning, knowledge distillation, and neu

embedding

Standalone embedding service for semantic search. Runs as persistent FastAPI server for millisecond-latency embeddings. Supports model swapping via env vars. Use when you need vectors for any database (ArangoDB, Pinecone, etc).

embedding-strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

embedding-strategy

PROTECTED - Chunking strategy and embedding dimension management

embeddings

Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking strategies, or building document similarity features.

engineering-features-for-machine-learning

Create, encode, transform, and select features before model fitting. Use when the user needs feature engineering decisions or implementation, not final training ownership or leakage auditing.

enzyme-autodiff

Enzyme.jl Automatic Differentiation Skill

esm

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect…

esm

Toolkit for protein language models (ESM3 for multimodal generative protein design; ESM C for efficient embeddings). Use when you need sequence/structure/function generation or prediction, inverse folding, protein embeddings, or scalable inference via local…

eval-and-benchmarking

LLM and ML model evaluation with lm-evaluation-harness, HELM, and custom benchmarks. Covers metric selection, contamination detection, statistical significance, and leaderboard methodology.

evaluate-model

Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.

evaluating-machine-learning-models

Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

event-detection-temporal-intelligence-expert

Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event…

examensvorbereitung-fragen

Examensvorbereitungs-Fragen für 1. und 2. Staatsexamen erstellen: Anwendungsfall Student will Examenswissen durch gezielte Uebungsfragen trainieren und Schwachstellen erkennen. 1. StEx und 2. StEx, JAG Bundesland Bayern NRW Hamburg, Subsumtion Gutachtenstil.…

Source text: German

experiment-configuration-with-hydra-yaml

Recommends using Hydra or YAML for experiment configuration to ensure clarity and reproducibility.

experiment-tracker

Manages ML experiment tracking with MLflow, Weights & Biases, or SpecWeave's built-in tracking. Activates for "track experiments", "MLflow", "wandb", "experiment logging", "compare experiments", "hyperparameter tracking". Automatically configures tracking…

explainable-ai

Make AI model decisions interpretable and transparent. Use for: implementing SHAP for feature importance analysis, using LIME for local explanations, creating attention visualizations for deep learning, generating counterfactual explanations, building…

extract-hyperparameters

Identify and document model hyperparameters from papers. Use when setting up training configurations.

faion-embeddings-skill

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faion-finetuning-skill

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faion-ml-engineer

ML Engineer role: LLM APIs (OpenAI, Claude, Gemini), embeddings, RAG pipelines, fine-tuning, LangChain, LlamaIndex, vector databases (Pinecone, Chroma, Weaviate), prompt engineering, model evaluation, cost optimization.

faion-ml-ops

ML operations: fine-tuning (LoRA, QLoRA), model evaluation, cost optimization, observability.

faion-multimodal-ai

Multimodal AI: vision, image/video generation, speech-to-text, text-to-speech, voice synthesis.

faion-rag-engineer

RAG engineering: embeddings, chunking, vector databases, hybrid search, reranking.

faion-vector-db-skill

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faion-video-gen-skill

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fal-image-edit

AI-powered image editing with style transfer and object removal

fal

Generate images, videos, audio, and more using fal.ai AI models. Use when user requests: "generate image", "create video", "make a picture", "text to image", "image to video", "text to speech", "transcribe audio", "edit image", "remove background", "upscale…

bio-fastq-quality

Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.

bio-workflows-fastq-to-variants

End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.

bio-workflows-fastq-to-variants

End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller.

feature-engineer

Comprehensive feature engineering for ML pipelines: data quality assessment, feature creation, selection, transformation, and encoding. Activates for "feature engineering", "create features", "feature selection", "data preprocessing", "handle missing values",…

feature-store-design

Online/offline feature serving, point-in-time correctness, Feast patterns, and feature computation design.

bio-rna-quantification-featurecounts-counting

Count reads per gene from aligned BAM files using Subread featureCounts. Use when you have BAM files from STAR/HISAT2 and need gene-level counts for DESeq2/edgeR.

federated-learning

Train models across distributed clients with privacy-preserving federated algorithms

fetch-arxiv-papers

Fetch recent AI/ML research papers from arXiv RSS feeds and store them in memory. Uses RSS tool for fetching and memory MCP for storage and deduplication.

fibonacci-optimization

Optimización evolutiva y paramétrica basada en Fibonacci y el Número Áureo (phi).

Source text: Spanish