GitHub
decision-support
Facilitate data-driven technical decisions using embedded decision matrices and trade-off analysis. Use when relevant to the task.
☆ 79Voir le skill
Skills IA tools machine learning
Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools machine learning
tools machine learning Effacer les filtres
Explorer
GitHub
data-analysis
Statistical analysis, outlier detection, and data transformation utilities.
☆ 76Voir le skill
GitHub
tensorflow-model-deployment
Deploy and serve TensorFlow models
☆ 75Voir le skill
GitHub
tensorflow-neural-networks
Build and train neural networks with TensorFlow
☆ 75Voir le skill
GitHub
discover-ml
Automatically discover machine learning and AI skills when working with machine learning. Activates for ml development tasks.
☆ 75Voir le skill
GitHub
task-classifier
Classify task complexity (1-10) to route to optimal model. Use when starting any non-trivial task to determine resources and approach. Triggers include: 'classify', 'complexity', 'what model', 'how complex'.
☆ 74Voir le skill
GitHub
thinking
深度推理，第一性原理分析
☆ 71Voir le skill
GitHub
thinking
深度推理，复杂决策和架构设计
☆ 71Voir le skill
GitHub
thinking
深度推理，第一性原理分析
☆ 71Voir le skill
GitHub
thinking
深度推理，逐步思考，第一性原理
☆ 71Voir le skill
GitHub
thinking
深度推理，逐步思考，第一性原理
☆ 71Voir le skill
GitHub
rnow-config
Configure ReinforceNow training runs with config.yml and train.jsonl. Use when setting up training configuration, choosing models, configuring RL algorithms, rollout settings, or training data format. Triggers...
☆ 69Voir le skill
GitHub
rnow-dataset
Convert HuggingFace datasets to ReinforceNow format. Use when creating train.jsonl from HuggingFace datasets, formatting data for SFT or RL training, or writing reward functions for math datasets. Triggers on "...
☆ 69Voir le skill
GitHub
rnow-cli
Use the ReinforceNow CLI for RLHF training. Use when running rnow commands, initializing projects, submitting training runs, testing rollouts, or downloading models. Triggers on "rnow", "rnow init", "rnow run",...
☆ 69Voir le skill
GitHub
ml-engineer
Implement ML pipelines, model serving, and feature engineering. Use for ML model integration or production deployment.
☆ 69Voir le skill
GitHub
prompt-engineer
Optimize prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts.
☆ 69Voir le skill
GitHub
ai-engineer
Use when building production-grade GenAI, Agentic Systems, Advanced RAG, or setting up rigorous Evaluation pipelines.
☆ 63Voir le skill
GitHub
fine-tuning-customization
LLM fine-tuning with LoRA, QLoRA, DPO alignment, and synthetic data generation. Efficient training, preference learning, data creation. Use when customizing models for specific domains.
☆ 61Voir le skill
GitHub
ollama-local
Local LLM inference with Ollama. Use when setting up local models for development, CI pipelines, or cost reduction. Covers model selection, LangChain integration, and performance tuning.
☆ 61Voir le skill
GitHub
anthropic-architect
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
☆ 61Voir le skill
GitHub
ml-cv-specialist
Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
☆ 56Voir le skill
GitHub
backend-models
Your approach to handling backend models. Use this skill when working on files where backend models comes into play.
☆ 52Voir le skill
GitHub
balls
Decomposed reasoning with explicit confidence scoring
☆ 48Voir le skill
GitHub
rstan-to-pystan
Guidance for converting R-Stan (RStan) code to Python-Stan (PyStan). This skill applies when translating Stan models and inference code from R to Python, including API mapping between RStan and PyStan 3.x, hype...
☆ 46Voir le skill
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.