machine-learning
12.5K
flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
ruvnet
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
11.9K
kfl
KFL2 (Kubeshark Filter Language) reference. This skill MUST be loaded before writing, constructing, or suggesting any KFL filter expression. KFL is statically typed — incorrect field names or syntax will fail silently or error. Do not guess at KFL syntax without this skill loaded. Trigger on any mention of KFL, CEL filters, traffic filtering, display filters, query syntax, filter expressions, write a filter, construct a query, build a KFL, create a filter expression, "how do I filter", "show me only", "find traffic where", protocol-specific queries (HTTP status codes, DNS lookups, Redis commands, Kafka topics), Kubernetes-aware filtering (by namespace, pod, service, label, annotation), L4 connection/flow filters, time-based queries, or any request to slice/search/narrow network traffic in Kubeshark. Also trigger when other skills need to construct filters — KFL is the query language for all Kubeshark traffic analysis.
kubeshark
data-ai
machine-learning
11.6K
review-checklist
Pre-merge review checklist based on recurring AI reviewer feedback patterns
nearai
data-ai
machine-learning
11.3K
evals
Objective eval metrics via code/model/human graders with pass@k/pass^k scoring. USE WHEN eval, evaluate, test agent, benchmark, verify behavior, regression test, capability test, run eval, compare models, compare prompts, create judge, create use case, view results, failure to task, suite manager, transcript capture, trial runner.
danielmiessler
data-ai
machine-learning
11.1K
ai-sdk-model-manager
Manages AI SDK model configurations - updates packages, identifies missing models, adds new models with research, and updates documentation
tambo-ai
data-ai
machine-learning
10.9K
nlp-alignment
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
aiming-lab
data-ai
machine-learning
10.9K
nlp-pretraining
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
aiming-lab
data-ai
machine-learning
10.9K
rl-policy-optimization
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
aiming-lab
data-ai
machine-learning
10.9K
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab
data-ai
machine-learning
10.4K
senior-ml-engineer
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
alirezarezvani
data-ai
machine-learning
10.4K
senior-prompt-engineer
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
alirezarezvani
data-ai
machine-learning
10.4K
scenario-war-room
Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when facing complex risk scenarios, strategic decisions with major downside, or when the user asks 'what if X AND Y both happen?'
alirezarezvani
data-ai
machine-learning
10.4K
senior-computer-vision
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
alirezarezvani
data-ai
machine-learning
10.4K
senior-data-scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.
alirezarezvani
data-ai
machine-learning
10.1K
huggingface-community-evals
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
huggingface
data-ai
machine-learning
10.1K
huggingface-llm-trainer
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
huggingface
data-ai
machine-learning
10.1K
huggingface-vision-trainer
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
huggingface
data-ai
machine-learning
10.1K
transformers-js
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
huggingface
data-ai
machine-learning
10.1K
gs-quant-backtesting
Guide to the gs_quant backtesting framework — engines, triggers, actions, strategies, and result extraction. Covers GenericEngine (multi-asset OTC), EquityVolEngine, and PredefinedAssetEngine.
goldmansachs
data-ai
machine-learning
9.8K
update-models
Use this skill when you need to update the AI models on the project.
baptisteArno
data-ai
machine-learning
9.2K
phoenix-evals-new-metric
Create a new built-in classification evaluator for Phoenix evals. Use this skill whenever the user asks to create a new eval, build a new metric, add a new builtin evaluator, create an LLM-as-a-judge metric, or add a new classification evaluator to Phoenix.
Arize-ai
data-ai