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.
45.100 de skill-uri din 2.196 de depozite · actualizat acum 2 ore
45.098 de skill-uri
- proof-checkerwanshuiyin/Auto-claude-code-research-in-sleepRigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via cross-model review (external reviewer backend, ultra reasoning), fixes each gap with full derivations, re-reviews, and generates an audit report. Use when user says "检查证明", "verify proof", "proof check", "审证明", "check this proof", or wants rigorous mathematical verification of a theory paper.ȘtiințăFără avertismente★ 18,1 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↓ —
- claims-draftingwanshuiyin/Auto-claude-code-research-in-sleepDraft patent claims for an invention. Use when user says "撰写权利要求", "draft claims", "写权利要求书", "claim drafting", or wants to create patent claims. The core skill of the patent pipeline.AlteleFără avertismente★ 18,1 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↓ —
- llamaguardOrchestra-Research/AI-Research-SKILLsMeta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.DevOpsFără avertismente★ 14 K↓ —
- slack-to-teamsmicrosoft/skillsSub-skill of microsoft-365-agents-toolkit. Routed expert system with 100+ micro-expert files for migrating Slack bots to Teams, cross-platform bridging, and dual-platform bot development. USE FOR: migrating Slack bot to Teams, adding Teams support to Slack bot, building dual-platform bots, converting Block Kit to Adaptive Cards, identity/OAuth bridging, deploying bots to Azure or AWS, configuring AI model providers. DO NOT USE FOR: general web development, non-bot projects, standalone Teams development without Slack (use parent skill instead).OficialDevOpsFără avertismente★ 3,1 K↓ —
- llama-cppOrchestra-Research/AI-Research-SKILLsRuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.DevOpsFără avertismente★ 14 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★ 14 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★ 14 K↓ —
- guidanceOrchestra-Research/AI-Research-SKILLsControl LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation frameworkȘtiințăFără avertismente★ 14 K↓ —
- kill-argumentwanshuiyin/Auto-claude-code-research-in-sleepTwo-thread adversarial review: a fresh reviewer constructs the strongest 200-word rejection memo, then a second fresh reviewer defends the paper point-by-point and surfaces still-unresolved critical issues. Use when user says "kill argument", "adversarial review", "hostile review", "rebuttal preparation", "reviewer-2 simulation", or before submitting a theory paper that has already passed standard review rounds.ȘtiințăFără avertismente★ 18,1 K↓ —
- microsoft-azure-webjobs-extensions-authentication-events-dotnetmicrosoft/skillsMicrosoft Entra Authentication Events SDK for .NET. Azure Functions triggers for custom authentication extensions. Use for token enrichment, custom claims, attribute collection, and OTP customization in Entra ID. Triggers: "Authentication Events", "WebJobsAuthenticationEventsTrigger", "OnTokenIssuanceStart", "OnAttributeCollectionStart", "custom claims", "token enrichment", "Entra custom extension", "authentication extension".OficialDevOpsFără avertismente★ 3,1 K↓ —
- customizemicrosoft/skillsInteractive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).OficialDevOpsFără avertismente★ 3,1 K↓ —Apare și în 3 alte depozite
- 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★ 14 K↓ —
- evaluating-cosmos-policyOrchestra-Research/AI-Research-SKILLsEvaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.MediaFără avertismente★ 14 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★ 14 K↓ —
- k2cloudflare/skillsBuild and troubleshoot Cloudflare K2 or K2 Streams durable logs. Use for stream setup, producing from Workers or HTTP, configuring retention and inputs, and consuming through subscriptions.OficialDevOpsFără avertismente★ 3,2 K↓ —Apare și în 1 alt depozit
- 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★ 14 K↓ —
- azure-microsoft-playwright-testing-tsmicrosoft/skillsRun Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipelines, or publishing test results to the Azure portal.OficialTestareFără avertismente★ 3,1 K↓ —
- azure-mgmt-arizeaiobservabilityeval-dotnetmicrosoft/skillsAzure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations on Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability into .NET applications. Triggers: "Arize AI", "ML observability", "ArizeAIObservabilityEval", "Arize organization".OficialDevOpsFără avertismente★ 3,1 K↓ —
- azure-kubernetes-app-deploymicrosoft/skillsUse when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.OficialDevOpsFără avertismente★ 3,1 K↓ —Apare și în 3 alte depozite
- auto-review-loopwanshuiyin/Auto-claude-code-research-in-sleepAutonomous multi-round research review loop. In Copilot CLI it defaults to the native complementary rubber-duck subagent with host-event model evidence; elsewhere it uses Codex, while explicit external reviewer overrides remain available. Implements fixes and re-reviews until a policy-approved positive assessment or max rounds is reached.InginerieFără avertismente★ 18,1 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★ 14 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★ 14 K↓ —
pyvene-interventions
Orchestra-Research/AI-Research-SKILLs
TestareActualizat 11 oct. 2026
De la autor
Provides 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.
Instalare
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pyvene-interventions -a claude-code
Se instalează în .claude/skills/pyvene-interventions/ · 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/README.md
- references/api.md
- references/tutorials.md