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.
38.727 de skill-uri din 1.780 de depozite · actualizat acum 4 minute
38.727 de skill-uri
- gitlab-cisickn33/agentic-awesome-skillsConfigure GitLab CI/CD pipelines and runners for automated building, testing, and deployment.TestareFără avertismente★ 48,4 K↓ —
- system-profilewanshuiyin/Auto-claude-code-research-in-sleepProfile a target (script, process, GPU, memory, interconnect) for performance analysis. Use when user says "profile", "benchmark", "bottleneck", or wants performance analysis.ProductivitateFără avertismente★ 18 K↓ —Apare și în 1 alt depozit
- wiki-enrichwanshuiyin/Auto-claude-code-research-in-sleepFill in the per-paper TODO sections of research-wiki/papers/<slug>.md pages that literature-ingest skills leave as bare scaffolds. Use when user says 'enrich wiki', 'fill paper TODOs', 'wiki body 補完', '把 paper 摘要寫進 wiki', 'research-wiki 自動填', or after a batch ingest that left papers/ as TODO scaffolds.ȘtiințăFără avertismente★ 18 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↓ —
- 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↓ —
- 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↓ —
- playwright-interactiveopenai/skillsPersistent browser and Electron interaction through `js_repl` for fast iterative UI debugging.OficialTestareFără avertismente★ 28,3 K↓ —
- google-ads-api-quickstartgoogle/skillsGuides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a "retrieve campaigns" script. Troubleshoots common setup errors: USER_PERMISSION_DENIED, login_customer_id issues, and DEVELOPER_TOKEN_NOT_APPROVED. Use this skill when: - The user asks how to get started with the Google Ads API. - The user needs to set up Google Ads credentials or developer tokens. - The user wants to write a quickstart/example script for Google Ads. - The user encounters errors like USER_PERMISSION_DENIED or DEVELOPER_TOKEN_NOT_APPROVED.OficialMarketingFără avertismente★ 21,5 K↓ —
- auto-paper-improvement-loopwanshuiyin/Auto-claude-code-research-in-sleepAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.ȘtiințăFără avertismente★ 18 K↓ —
- paper-writingwanshuiyin/Auto-claude-code-research-in-sleepWorkflow 3: Full paper writing pipeline. Orchestrates paper-plan → paper-figure → paper-write → paper-compile → auto-paper-improvement-loop to go from a narrative report to a polished, submission-ready PDF. Use when user says \"写论文全流程\", \"write paper pipeline\", \"从报告到PDF\", \"paper writing\", or wants the complete paper generation workflow.ȘtiințăFără avertismente★ 18 K↓ —
- experiment-auditwanshuiyin/Auto-claude-code-research-in-sleepAudit experiment integrity before claiming results. Uses cross-model review (external reviewer backend) 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↓ —
- 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★ 13,9 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★ 13,9 K↓ —
- golden-jupyteryusufkaraaslan/Skill_SeekersUse when testing the golden_jupyter golden buildTestareFără avertismente★ 15,2 K↓ —
- pytorch-fsdp2Orchestra-Research/AI-Research-SKILLsAdds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.ProductivitateFără avertismente★ 13,9 K↓ —
- golden-pptx-kwyusufkaraaslan/Skill_SeekersUse when testing keyword categorizationDocumenteFără avertismente★ 15,2 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★ 13,9 K↓ —
- moe-trainingOrchestra-Research/AI-Research-SKILLsTrain Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.PlanificareFără avertismente★ 13,9 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★ 13,9 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★ 13,9 K↓ —
- golden-rss-emptyyusufkaraaslan/Skill_SeekersUse when testing the empty rss golden buildTestareFără avertismente★ 15,2 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★ 13,9 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★ 13,9 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★ 13,9 K↓ —
azure-containerregistry-py
microsoft/skills
OficialDevOpsActualizat ieri
De la autor
Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories. Triggers: "azure-containerregistry", "ContainerRegistryClient", "container images", "docker registry", "ACR".
Instalare
npx skills add microsoft/skills --skill azure-containerregistry-py -a claude-code
Se instalează în .claude/skills/azure-containerregistry-py/ · 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
Proveniență100
Întreținere100
Licență100
Adopție56
Fișiere (3)
- SKILL.md
- references/capabilities.md
- references/non-hero-scenarios.md