Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
39.063 de skill-uri din 1.798 de depozite · actualizat acum 11 minute
39.063 de skill-uri
- code-mate-qoderCherryHQ/cherry-studioRun the Qoder CLI non-interactively with structured output. Use when the user asks to delegate a coding task to Qoder.InginerieFără avertismente★ 52,9 K↓ —
- serving-llms-vllmOrchestra-Research/AI-Research-SKILLsServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.DevOpsFără avertismente★ 13,9 K↓ —
- wiki-qamicrosoft/skillsAnswers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating the codebase.OficialTestareFără avertismente★ 3,1 K↓ —
- cost-analysismicrosoft/skillsAnalyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.OficialDevOpsFără avertismente★ 3,1 K↓ —Apare și în 1 alt depozit
- wiki-onboardingmicrosoft/skillsGenerates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Use when the user wants onboarding documentation for a codebase.OficialAlteleFără avertismente★ 3,1 K↓ —
- miles-rl-trainingOrchestra-Research/AI-Research-SKILLsProvides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.PlanificareFără avertismente★ 13,9 K↓ —
- axolotlOrchestra-Research/AI-Research-SKILLsExpert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal supportAlteleFără avertismente★ 13,9 K↓ —
- optimizing-attention-flashOrchestra-Research/AI-Research-SKILLsOptimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.ProductivitateFără avertismente★ 13,9 K↓ —
- fine-tuning-serving-openpiOrchestra-Research/AI-Research-SKILLsFine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.InginerieFără avertismente★ 13,9 K↓ —
- verl-rl-trainingOrchestra-Research/AI-Research-SKILLsProvides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.DevOpsFără avertismente★ 13,9 K↓ —
- simpo-trainingOrchestra-Research/AI-Research-SKILLsSimple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.AlteleFără avertismente★ 13,9 K↓ —
- slime-rl-trainingOrchestra-Research/AI-Research-SKILLsProvides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.AlteleFără avertismente★ 13,9 K↓ —
- continual-learningmicrosoft/skillsGuide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.OficialDevOpsFără avertismente★ 3,1 K↓ —
- github-issue-creatormicrosoft/skillsConvert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.OficialProductivitateFără avertismente★ 3,1 K↓ —
- gptqOrchestra-Research/AI-Research-SKILLsPost-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.DevOpsFără avertismente★ 13,9 K↓ —
- llamaindexOrchestra-Research/AI-Research-SKILLsData framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.PlanificareFără avertismente★ 13,9 K↓ —
- blip-2-vision-languageOrchestra-Research/AI-Research-SKILLsVision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.MediaFără avertismente★ 13,9 K↓ —
- frontend-design-reviewmicrosoft/skillsReview and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.OficialDesignFără avertismente★ 3,1 K↓ —
- langchainOrchestra-Research/AI-Research-SKILLsFramework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.DevOpsFără avertismente★ 13,9 K↓ —
- hqq-quantizationOrchestra-Research/AI-Research-SKILLsHalf-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.DevOpsFără avertismente★ 13,9 K↓ —
- llavaOrchestra-Research/AI-Research-SKILLsLarge Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.MediaFără avertismente★ 13,9 K↓ —
- peft-fine-tuningOrchestra-Research/AI-Research-SKILLsParameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.ProductivitateFără avertismente★ 13,9 K↓ —
- awq-quantizationOrchestra-Research/AI-Research-SKILLsActivation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.DevOpsFără avertismente★ 13,9 K↓ —
- gguf-quantizationOrchestra-Research/AI-Research-SKILLsGGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.DevOpsFără avertismente★ 13,9 K↓ —
weights-and-biases
Orchestra-Research/AI-Research-SKILLs
AlteleActualizat 11 oct. 2026
De la autor
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Instalare
npx skills add Orchestra-Research/AI-Research-SKILLs --skill weights-and-biases -a claude-code
Se instalează în .claude/skills/weights-and-biases/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniFolosește rețeauaLicență: MIT
Verificări de siguranță pe SKILL.md
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De ce 93
Securitate100
Proveniență90
Întreținere100
Licență100
Adopție65
Fișiere (4)
- SKILL.md
- references/artifacts.md
- references/integrations.md
- references/sweeps.md