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.
36.321 de skill-uri din 1.649 de depozite · actualizat acum 6 minute
36.321 de skill-uri
- 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★ 13,9 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↓ —
- 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↓ —
- patent-novelty-checkwanshuiyin/Auto-claude-code-research-in-sleepAssess patent novelty and non-obviousness against prior art. Use when user says "专利查新", "patent novelty", "可专利性评估", "patentability check", or wants to evaluate if an invention is patentable.AlteleFără avertismente★ 18 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↓ —
- huggingface-tokenizersOrchestra-Research/AI-Research-SKILLsFast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.ȘtiințăFără avertismente★ 13,9 K↓ —
- wiki-researchermicrosoft/skillsConducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.OficialȘtiințăFără avertismente★ 3,1 K↓ —
- khazix-writerKKKKhazix/khazix-skills数字生命卡兹克（Khazix）的公众号长文写作skill。当用户需要撰写公众号文章、写稿子、续写文章、根据素材产出长文时使用。触发词包括但不限于：写文章、写稿子、帮我写、续写、扩写、公众号文章、长文、出稿、按我的风格写。即使用户只是说"帮我把这个写成文章"或"用我的风格写一下"，只要上下文涉及内容创作和公众号输出，都应该触发。也适用于用户丢过来一个PDF、brief、新闻链接、语音转文字或任何素材说"帮我写篇文章"的场景。不要用于短内容（小红书帖子、推特、朋友圈）或纯标题摘要生成（那个用wechat-title skill）。RedactareFără avertismente★ 21,6 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↓ —
- code-mate-deepseek-harnessCherryHQ/cherry-studioRun DeepSeek Harness non-interactively for coding tasks. Use when the user asks to delegate work to it.InginerieFără avertismente★ 52,9 K↓ —
- leaderKKKKhazix/khazix-skills把一句话的想法拆成 AI agent 能独立跑完的目标任务书。用户说「帮我给 agent 写个目标」「帮我详细拆一下这个目标」「写个任务书/brief 给 agent」「写个 goal 提示词」「让 agent 自己跑这个项目」「把活分给几个 agent 并行」时使用。先进代码库实测、必要时联网调研，再一次性提问（≤5 个），产出一份 ≤4000 字符、直接粘进 /goal 就能跑的任务书，含实测数字、白名单地界、防作弊验收和断点续跑。执行型与探索型（调研/选型/找方案）自动分流。AlteleFără avertismente★ 21,6 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↓ —
- 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↓ —
- idea-discovery-robotwanshuiyin/Auto-claude-code-research-in-sleepWorkflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says "robotics idea discovery", "机器人找idea", "embodied AI idea", "机器人方向探索", "sim2real 选题", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.InginerieFără avertismente★ 18 K↓ —
- experiment-queuewanshuiyin/Auto-claude-code-research-in-sleepSSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is insufficient for 10+ jobs that need orchestration.AlteleFără avertismente★ 18 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↓ —
- 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↓ —
- code-mate-github-copilotCherryHQ/cherry-studioRun GitHub Copilot CLI non-interactively for coding tasks. Use when the user asks to delegate work to Copilot.InginerieFără avertismente★ 52,9 K↓ —
- interview-cheatsheetwanshuiyin/Auto-claude-code-research-in-sleepGenerate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab). Use when the user says '写面试 cheat sheet', '写一份 X 教程', '帮我准备 Y 面试题', '出一份 X 速查', or wants a 600-1000 line Chinese tutorial on a specific ML topic.PlanificareFără avertismente★ 18 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↓ —
- 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↓ —
- pyvene-interventionsOrchestra-Research/AI-Research-SKILLsProvides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.TestareFără avertismente★ 13,9 K↓ —
- experiment-auditwanshuiyin/Auto-claude-code-research-in-sleepAudit experiment integrity before claiming results. Uses fresh-agent GPT-6-Astra review (same-family provisional in the base Codex mirror) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.RedactareFără avertismente★ 18 K↓ —
auto-review-loop-minimax
wanshuiyin/Auto-claude-code-research-in-sleep
InginerieActualizat acum 3 zile
De la autor
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
Instalare
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-minimax -a claude-code
Se instalează în .claude/skills/auto-review-loop-minimax/ · 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
Niciun avertisment.
De ce 93
Securitate100
Proveniență91
Întreținere100
Licență100
Adopție66
Fișiere (1)
- SKILL.md