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meta-analyst-agent
AI self-improvement analyst. Tracks AI agent mistakes, analyzes failure patterns, and proposes system improvements. Implements continuous learning loop for trading system enhancement.
Skills IA tools machine learning
Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools machine learning
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torchserve
Model serving engine for PyTorch. Focuses on MAR packaging, custom handlers for preprocessing/inference, and management of multi-GPU worker scaling. (torchserve, mar-file, handler, basehandler, model-archiver,...
GitHub
unsloth-models
Guidance on selecting and configuring supported model architectures like Llama 4, DeepSeek-R1, and Qwen3. Triggers: llama 4, deepseek-r1, qwen3, gemma 3, model selection, instruct vs base.
GitHub
unsloth-fft
Performing full fine-tuning (FFT) in Unsloth with 100% exact weight updates and optimized gradient checkpointing. Triggers include fft, full fine-tuning, full_finetuning, exact fine-tuning, and weight updates.
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pytorch-geometric
Library for Graph Neural Networks (GNNs). Covers MessagePassing layers, modular aggregation schemes, and handling large graphs via mini-batching with disjoint graph representation. (pyg, messagepassing, gnn, gc...
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eval-frameworks
Evaluation framework patterns for RAG and LLMs, including faithfulness metrics, synthetic dataset generation, and LLM-as-a-judge patterns. Triggers: ragas, deepeval, llm-eval, faithfulness, hallucination-check,...
GitHub
unsloth-lora
Configuring and optimizing 16-bit Low-Rank Adaptation (LoRA) and Rank-Stabilized LoRA (rsLoRA) for efficient LLM fine-tuning using triggers like lora, qlora, rslora, rank selection, lora_alpha, lora_dropout, an...
GitHub
unsloth-long-context
Training models on extended context lengths using optimized RoPE scaling and memory-efficient attention kernels. Triggers: long context, max_seq_length, rope scaling, large context window, flex attention.
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unsloth-sft
Supervised fine-tuning using SFTTrainer, instruction formatting, and multi-turn dataset preparation with triggers like sft, instruction tuning, chat templates, sharegpt, alpaca, conversation_extension, and SFTT...
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pytorch-lightning
High-level training framework for PyTorch that abstracts boilerplate while maintaining flexibility. Includes the Trainer, LightningModule, and support for multi-GPU scaling and reproducibility. (lightning, pyto...
GitHub
unsloth-dpo
Direct Preference Optimization (DPO) for aligning models with preference data without separate reward models. Triggers: dpo, preference optimization, rlhf, ref_model=none, patchdpotrainer, dpotrainer.
GitHub
sota-data-cleaning-feature-selection-eda
Master SOTA data prep for Kaggle comps: automated EDA (Sweetviz), cleaning (Pyjanitor), and feature selection (Polars + XGBoost) for medium datasets (100MB–5GB) in Colab.
GitHub
experiment-tracking
Integrate Trackio for experiment tracking in Kaggle competitions. Use PROACTIVELY when user trains models, logs metrics, or manages experiments. Keywords: 実験, 訓練, train, training, tracking, metrics, 指標,...
GitHub
kaggle-workflow
Guide through complete Kaggle competition workflow from TODO updates to submission. Use PROACTIVELY when user starts new experiments, prepares submissions, or asks about competition workflow. Keywords: Kaggle,...
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performance-optimization
Optimize training performance for Kaggle competitions. Use PROACTIVELY when training is slow, GPU memory issues occur, or user asks about performance tuning. Keywords: 遅い, 最適化, OOM, CUDA, batch_size, num_w...
GitHub
ai-dev-guidelines
Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orc...
GitHub
crossvit-covid19-fyp
Complete context for TAR UMT Data Science FYP implementing CrossViT for COVID-19 chest X-ray classification. Use when working on Jupyter notebooks, code implementation, data analysis, model training, or any tas...
GitHub
data-viz-insight
Interactive data exploration and visualization skill. Use when users ask to visualize data, analyze datasets, create charts, or explore data files (CSV, Excel, Parquet, JSON). This skill guides through data exp...
GitHub
yanex-experiment-tracking
Use this skill when running, managing, or analyzing yanex experiments. Includes executing experiments via CLI, parameter sweeps, dependencies, querying experiment history, comparing results, and maintaining exp...
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scarches
Comprehensive skill for scARCHES - Deep learning library for single-cell analysis of chromatin accessibility data. Use for scATAC-seq data processing, model training, batch correction, integration with scRNA-se...
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hti-zen-orchestrator
Guidelines for using Zen MCP tools effectively in this repo. Use for complex multi-model tasks, architectural decisions, or when cross-model validation adds value.
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production-api-tester
Live testing and validation of production research API for strategy optimization loops
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fine-tune
Use when you need to fine-tune(ファインチューニング) and optimize LangGraph applications based on evaluation criteria. This skill performs iterative prompt optimization for LangGraph nodes without changing the...
GitHub
reward-shaping-engineering
Master reward function design - potential-based shaping, hacking patterns, validation
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.