skills.homescapability registry تلاش

home/categories/machine-learning

category focus

Machine Learning

Training models and neural networks.

1987 اسکلزall categories

sorting

stars

current ordering strategy

query

all entries

refine the visible subset

machine-learning

1

multi-model-validation

Run multiple AI models in parallel for 3-5x speedup with ENFORCED performance statistics tracking. Use when validating with Grok, Gemini, GPT-5, DeepSeek, or Claudish proxy for code review, consensus analysis, or multi-expert validation. NEW in v3.1.0 - SubagentStop hook enforces statistics collection, MANDATORY checklist prevents incomplete reviews, timing instrumentation examples. Includes dynamic model discovery via `claudish --top-models` and `claudish --free`, session-based workspaces, and Pattern 7-8 for tracking model performance. Trigger keywords - "grok", "gemini", "gpt-5", "deepseek", "claudish", "multiple models", "parallel review", "external AI", "consensus", "multi-model", "model performance", "statistics", "free models".

involvex

data-ai

machine-learning

1

training-hub

Fine-tune LLMs using Red Hat training-hub library with SFT, LoRA, and OSFT algorithms. Use when preparing JSONL datasets, running training jobs, configuring hardware, scaling to clusters, evaluating models, or deploying with vLLM.

RobbieJ

data-ai

machine-learning

1

training-data

Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.

doanchienthangdev

data-ai

machine-learning

1

ml-deployment

Deploy ML models to production - APIs, containerization, monitoring, and MLOps

pluginagentmarketplace

data-ai

machine-learning

1

model

Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.

muzhicaomingwang

data-ai

machine-learning

1

context-engineering

Elite context engineering specialist for AI agents - token optimization, degradation patterns, compression, memory systems, multi-agent coordination, vector databases, knowledge graphs, RAG systems, and enterprise context management. Use PROACTIVELY for complex AI orchestration, agent design, debugging context failures, or building LLM pipelines.

thienchi2109

data-ai

machine-learning

1

ai-engineering

Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems.

doanchienthangdev

data-ai

machine-learning

1

finetuning

Finetuning Foundation Models - when to finetune, LoRA, QLoRA, PEFT techniques, memory optimization, model merging. Use when adapting models to specific domains, reducing costs, or improving performance.

doanchienthangdev

data-ai

machine-learning

1

foundation-models

Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.

doanchienthangdev

data-ai

machine-learning

1

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 utilities (Sklearn preprocessing, metrics, splits), conformal prediction (MAPIE), or model explainability (SHAP). Triggers for: (1) Writing East programs with @elaraai/east-py-datascience, (2) Derivative-free optimization with MADS, (3) Bayesian optimization with Optuna, (4) Discrete/combinatorial optimization with SimAnneal, (5) Gradient boosting with XGBoost or LightGBM, (6) Probabilistic predictions with NGBoost or GP, (7) Neural networks with Torch MLP or Lightning, (8) Data preprocessing and metrics with Sklearn, (9) Conformal prediction intervals with MAPIE, (10) Model explainability with Shap.

elaraai

data-ai

machine-learning

1

google-adk-python

Use when building AI agents with Google's Agent Development Kit (ADK) Python - multi-agent systems, workflow agents, tool integration, Vertex AI deployment, or agent evaluation.

GGPrompts

data-ai

machine-learning

1

model-extraction

Techniques to extract model weights, architecture, and training data through API queries

pluginagentmarketplace

data-ai

machine-learning

1

ml-systems-fundamentals

Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.

doanchienthangdev

data-ai

machine-learning

1

unsloth-finetuning

Fine-tune LLMs 2x faster with 80% less memory using Unsloth. Use when the user wants to fine-tune models like Llama, Mistral, Phi, or Gemma. Handles model loading, LoRA configuration, training, and model export.

ScientiaCapital

data-ai

machine-learning

1

huggingface-model-trainer

Train and fine-tune LLMs using HuggingFace TRL, Transformers, and cloud GPU infrastructure with SFT, DPO, GRPO methods

frankxai

data-ai

machine-learning

1

superbpe

Train and use SuperBPE tokenizers for 20-33% token reduction across any project. Covers training, optimization, validation, and integration with any LLM framework. Use when you need efficient tokenization, want to reduce API costs, or maximize context windows.

ScientiaCapital

data-ai

machine-learning

1

rust-candle-core

Build native Rust ML models with Candle framework. Use when implementing vision transformers, LLMs, or audio models with GPU acceleration.

gar-ai

data-ai

machine-learning

1

model-serving

Master model serving - inference optimization, scaling, deployment, edge serving

pluginagentmarketplace

data-ai

machine-learning

1

tinker

Comprehensive guide for Tinker Cookbook supervised fine-tuning covering all patterns including high-level Cookbook abstractions, low-level API usage, streaming datasets, file-based data, Blueprint configuration, and vision-language models.

M4n5ter

data-ai

machine-learning

1

ccg-latent

Use this skill for multi-step development tasks. Latent Chain Mode reduces token usage by 70-80% through hidden-state reasoning with 4-phase workflow: analysis → plan → impl → review.

phuongrealmax

data-ai

machine-learning

1

secure-deployment

Security best practices for deploying AI/ML models to production environments

pluginagentmarketplace

data-ai

machine-learning

1

inference-optimization

Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.

doanchienthangdev

data-ai

machine-learning

1

genai-dac-specialist

Expert in OCI Generative AI Dedicated AI Clusters - deployment, fine-tuning, optimization, and production operations

frankxai

data-ai

machine-learning

1

burn-training

This skill should be used when the user asks about "training loop", "Learner", "metrics", "dataset", "dataloader", "checkpointing", "optimizer", "learning rate scheduler", "custom training", or Burn training workflows.

johnzfitch

data-ai

Page 71 / 83