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.
45.186 de skill-uri din 2.196 de depozite · actualizat acum 9 minute
45.184 de skill-uri
- baijiahao-uploadNoudreammis/social-auto-upload使用 social-auto-upload 的 `sau baijiahao` 登录百家号、检查账号并发布视频。Use when 用户要向百家号发布视频或核验百家号登录状态。AlteleFără avertismente★ 15,8 K↓ —
- eas-app-storesexpo/skillsBuild and submit iOS and Android apps with EAS to TestFlight, the App Store, or Google Play. Supports Expo and other React Native projects, plus existing native apps. Use for eas.json setup, release pipelines, signing, app versions and build numbers, store submissions, and listing metadata. For Expo websites and API routes, use eas-hosting; for adding React Native screens to a native app, use expo-brownfield.OficialInginerieFără avertismente★ 3 K↓ —
- training-llms-megatronOrchestra-Research/AI-Research-SKILLsTrains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.ProductivitateFără avertismente★ 14 K↓ —
- constitutional-aiOrchestra-Research/AI-Research-SKILLsAnthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.AlteleFără avertismente★ 14 K↓ —
- ml-paper-writingOrchestra-Research/AI-Research-SKILLsWrite publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.ȘtiințăFără avertismente★ 14 K↓ —
- model-pruningOrchestra-Research/AI-Research-SKILLsReduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.AlteleFără avertismente★ 14 K↓ —
- model-mergingOrchestra-Research/AI-Research-SKILLsMerge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.DevOpsFără avertismente★ 14 K↓ —
- long-contextOrchestra-Research/AI-Research-SKILLsExtend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.ProductivitateFără avertismente★ 14 K↓ —
- translate-docsCherryHQ/cherry-studioUse when explicitly asked to translate, backfill, reconcile, rename, or delete paired Cherry Studio repository documentation in English and Simplified Chinese, including Phase 3 domain-by-domain translation work.AlteleFără avertismente★ 53 K↓ —
- nemo-guardrailsOrchestra-Research/AI-Research-SKILLsNVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.AlteleFără avertismente★ 14 K↓ —
- tensorrt-llmOrchestra-Research/AI-Research-SKILLsOptimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.DevOpsFără avertismente★ 14 K↓ —
- ray-trainOrchestra-Research/AI-Research-SKILLsDistributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.AlteleFără avertismente★ 14 K↓ —
- instructorOrchestra-Research/AI-Research-SKILLsExtract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output libraryAlteleFără avertismente★ 14 K↓ —
- fine-tuning-with-trlOrchestra-Research/AI-Research-SKILLsFine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.AlteleFără avertismente★ 14 K↓ —
- distributed-llm-pretraining-torchtitanOrchestra-Research/AI-Research-SKILLsProvides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.AlteleFără avertismente★ 14 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★ 14 K↓ —
- mamba-architectureOrchestra-Research/AI-Research-SKILLsState-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.PlanificareFără avertismente★ 14 K↓ —
- audiocraft-audio-generationOrchestra-Research/AI-Research-SKILLsPyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.MediaFără avertismente★ 14 K↓ —
- modal-serverless-gpuOrchestra-Research/AI-Research-SKILLsServerless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.DevOpsFără avertismente★ 14 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★ 14 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★ 14 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★ 14 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★ 14 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★ 14 K↓ —
cost-governance
microsoft/skills
De la autor
Govern Azure costs with budgets, alerts, tags, and policy restrictions. WHEN: "Azure budget health", "configured budget overrun", "list Azure budgets", "create Azure budget", "budget alerts", "budget coverage", "missing CostCenter tags", "allowed VM SKUs", "allowed Azure regions", "would this SKU be denied", "cost guardrails". DO NOT USE FOR: planning-target forecasts, bill investigations, pricing, rightsizing, or commitments.
Instalare
npx skills add microsoft/skills --skill cost-governance -a claude-code
Se instalează în .claude/skills/cost-governance/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Verificări de siguranță pe SKILL.md
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De ce 93
Fișiere (5)
- SKILL.md
- references/budget-health.md
- references/budget-setup.md
- references/guardrails.md
- references/tool-fallback.md