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 12 minute
39.063 de skill-uri
- segment-anything-modelOrchestra-Research/AI-Research-SKILLsFoundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.MediaFără avertismente★ 13,9 K↓ —
- prompt-guardOrchestra-Research/AI-Research-SKILLsMeta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.SecuritateFără avertismente★ 13,9 K↓ —
- sentence-transformersOrchestra-Research/AI-Research-SKILLsFramework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.MediaFără avertismente★ 13,9 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↓ —
- nemo-evaluator-sdkOrchestra-Research/AI-Research-SKILLsEvaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.DevOpsFără avertismente★ 13,9 K↓ —
- nanogptOrchestra-Research/AI-Research-SKILLsEducational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).PlanificareFără avertismente★ 13,9 K↓ —
- quantizing-models-bitsandbytesOrchestra-Research/AI-Research-SKILLsQuantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.ProductivitateFără avertismente★ 13,9 K↓ —
- kqlmicrosoft/skillsKQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.OficialDevOpsFără avertismente★ 3,1 K↓ —
- golden-jupyteryusufkaraaslan/Skill_SeekersUse when testing the golden_jupyter golden buildTestareFără avertismente★ 15,2 K↓ —
- implementing-llms-litgptOrchestra-Research/AI-Research-SKILLsImplements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.PlanificareFără avertismente★ 13,9 K↓ —
- llama-factoryOrchestra-Research/AI-Research-SKILLsExpert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal supportInginerieFără avertismente★ 13,9 K↓ —
- anatomywheresryan22/anatomyBuild crafted, highly detailed, interactive isometric SVG figures that explain a technical idea as a real physical object (a test rig, a dial gauge, a cabinet of drawers, a plotter, a lock), with many small precise solid parts, four-tone shading, correct painter's order, a framed card with title, hint and live readout, and calm eased motion. Use this whenever someone wants an isometric illustration, an explanatory diagram or figure for docs, a blog post, a landing page or an article, a "Linear-style" or "premium" technical drawing, an interactive explainer, or an SVG that shows how a system, algorithm or component works, even if they never say "isometric". Works framework-free (TypeScript/JS that writes SVG and HTML) and in React/Next.js, and can add realistic WebGL shaders (fire, exhaust, water, caustics, lamp light) that live inside the drawing in the same isometric perspective. Also prints a figure as an A4 PDF (poster, drawing or handout) and publishes it to the Anatomy community gallery when asked.TestareFără avertismente★ 19,9 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★ 13,9 K↓ —
- dspyOrchestra-Research/AI-Research-SKILLsBuild complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programmingAlteleFără avertismente★ 13,9 K↓ —
- langsmith-observabilityOrchestra-Research/AI-Research-SKILLsLLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.DevOpsFără avertismente★ 13,9 K↓ —
- torchforge-rl-trainingOrchestra-Research/AI-Research-SKILLsProvides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.AlteleFără avertismente★ 13,9 K↓ —
- sglangOrchestra-Research/AI-Research-SKILLsFast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.MarketingFără avertismente★ 13,9 K↓ —
- podmansickn33/agentic-awesome-skillsManage containers using Podman, the daemonless container engine.AlteleFără avertismente★ 48,4 K↓ —Apare și în 1 alt depozit
- fine-tuning-openvla-oftOrchestra-Research/AI-Research-SKILLsFine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.DevOpsFără avertismente★ 13,9 K↓ —
- code-mate-codexCherryHQ/cherry-studioRun Codex CLI non-interactively for coding tasks. Use when the user asks to delegate work to Codex.InginerieFără avertismente★ 52,9 K↓ —
- rwkv-architectureOrchestra-Research/AI-Research-SKILLsRNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.PlanificareFără avertismente★ 13,9 K↓ —
- wiki-queryAgriciDaniel/claude-obsidianAnswer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.ProductivitateFără avertismente★ 15,8 K↓ —
- golden-chat-emptyyusufkaraaslan/Skill_SeekersUse when testing the golden_chat_empty golden buildTestareFără avertismente★ 15,2 K↓ —
- skillkrillinai/OpenCreatorFormerly KrillinAI, VideoTranslation and Dubbing Tool. Open-source AI workspace for creators, powered by Codex. Video downloading, video translation, create videos, images, voice, avatars, and edits with Agents in one place. Suitable for your Social Media like YouTube, TikTok, Instagram, etc.MediaFără avertismente★ 13,9 K↓ —
quantizing-models-bitsandbytes
Orchestra-Research/AI-Research-SKILLs
ProductivitateActualizat 11 oct. 2026
De la autor
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
Instalare
npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes -a claude-code
Se instalează în .claude/skills/quantizing-models-bitsandbytes/ · 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
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Proveniență90
Întreținere100
Licență100
Adopție65
Fișiere (4)
- SKILL.md
- references/memory-optimization.md
- references/qlora-training.md
- references/quantization-formats.md