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.
47.966 de skill-uri din 2.309 depozite · actualizat acum 10 ore
47.966 de skill-uri
- skill-algorithm-updatesZJU-REAL/Easel追踪抖音、小红书、B站、微博、知乎和视频号的算法、推荐分发、审核与变现规则变化,并分析对创作者的影响。 当用户说“算法/推荐机制变了吗、流量规则更新、为什么流量下降、平台规则变化”时使用。 内容热点用 skill-trending-topics;泛行业资讯用 skill-news-intelligence;本 SKILL 只看平台机制。AlteleFără avertismente★ 4,2 K↓ —
- mbse-requirementsNoudavepoon/buildwithclaudeAuthor, review, and repair system requirements to aerospace and defense practice. Use when writing or fixing requirements, applying EARS patterns, assigning requirement IDs, setting priority or verification method, splitting compound requirements, removing ambiguity, or reviewing a requirements document for quality. Triggers on requirement, shall statement, EARS, spec review, acceptance criteria, requirements quality, ReqIF.PlanificareFără avertismente★ 3,7 K↓ —
- skill-content-calendarZJU-REAL/Easel生成月度社媒内容排期表:逐条选题+角度+视觉方向,覆盖小红书/抖音/B站/微博。 当用户说"内容排期""月度日历""发布计划""内容日历""排期表""这个月发什么""内容节奏表"时使用。 消费 skill-content-strategy 定义的支柱与 shared 频率基线,只出具体排期不重复定义支柱理论。ProductivitateFără avertismente★ 4,2 K↓ —
- i4h-workflow-setupNVIDIA/skillsPreflight and set up the root-level workflow runtime. Use for installation, missing component environments, or third-party failures; do not use for rollout validation.ProductivitateFără avertismente★ 3,8 K↓ —
- design-screenplugin87/ux-ui-agent-skillsDesign or build a page, screen, landing page, website, app or UI from a request - the whole job, end to end. Use this for "design a landing page", "build a settings screen", "make me a website", "design the onboarding", "ออกแบบหน้าเว็บ", "ทำหน้า landing", "ออกแบบ UI", "ทำแอป". It runs the pipeline no single skill covers: the house doctrine, a written Brief Inference so the result is not the statistical mean, a committed visual direction, tokens, build, the objective gates, then an adversarial critic. Hands off to data-dashboard for dense data screens, redesign for existing UI, and image-to-code when a reference image is given.DesignFără avertismente★ 3,1 K↓ —
- tao-finetune-kumo-anomalyNVIDIA/skillsKumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".AlteleFără avertismente★ 3,8 K↓ —
- trend-spotteraaron-he-zhu/aaron-marketing-skillsUse when the user asks to "find trending topics", "what trends should my brand jump on", or "time a campaign around a cultural moment"; produces a ranked trend report with brand-fit scores, format calls (rising/peak/declining), a cultural calendar, and go/skip recommendations. Not for finding the creators to run those trends — use influencer-discovery; not for building the brand posting calendar from a go verdict — use social-calendar-builder. 热点趋势洞察/借势营销MarketingFără avertismente★ 3 K↓ —
- tao-run-dinov3-ssl-deftNVIDIA/skillsRun iterative DINOv3 SSL DEFT data selection and retraining from an unlabeled pool, using GRIT or multi-task weakness and fixed-encoder retrieval. Use for active-learning-style DINOv3 mining, not plain pretraining/fine-tuning (tao-train-dinov3) or AOI/PAS DEFT.ProductivitateFără avertismente★ 3,8 K↓ —
- tao-run-inference-serviceNVIDIA/skillsStart, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.DevOpsFără avertismente★ 3,8 K↓ —
- codonfm-scoreNVIDIA/skillsValidate, prepare, or run public CodonFM Encodon masked-codon variant scoring and review compatibility of its scoring workflows. Use only when the user explicitly requests CodonFM or Encodon, or that context is already established in the conversation. Do not select this skill for a generic variant-scoring request without that context; ask for the variant and intended analysis first.InginerieFără avertismente★ 3,8 K↓ —
- codonfm-setupNVIDIA/skillsSet up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify GPU access, or download public Encodon 80M, 600M, 1B, or Cdwt-1B weights. Do not use for Decodon, Encodon 5B/10B, missense-aggregation, or codon-optimization setup because those implementations are not in the public repository.AlteleFără avertismente★ 3,8 K↓ —
- tao-train-mask-auto-labelNVIDIA/skillsMAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (point or box annotations) using a ViT-MAE backbone. Use when training, evaluating, or running inference for a TAO MAL model. Trigger phrases include "train MAL", "Mask Auto-Label", "weakly-supervised segmentation", "box-prompted segmentation", "minimal-annotation mask prediction".AlteleFără avertismente★ 3,8 K↓ —
- tao-train-nvdinov2NVIDIA/skillsNVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or running inference for a TAO NVDINOv2 backbone. Trigger phrases include "train NVDINOv2", "self-supervised ViT pretraining", "DINOv2 backbone", "visual representation learning".AlteleFără avertismente★ 3,8 K↓ —
- nvflare-diagnose-jobNVIDIA/skillsUse when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.AlteleFără avertismente★ 3,8 K↓ —
- jetson-video-capabilityNVIDIA/skillsUse when Jetson codec, profile, chroma, bit-depth, dimension, engine-count, or operational support must be reconciled from live APIs, authenticated NVIDIA samples, and NVIDIA documentation; also applies the content-DRM scope.MediaFără avertismente★ 3,8 K↓ —
- tao-train-codetrNVIDIA/skillsCo-DETR (CoDINO) for object detection. A DETR-family detector with collaborative hybrid assignment — auxiliary one-to-many heads supervise the encoder during training, giving strong closed-set accuracy at high inference cost. Use when training, evaluating, or running inference for a TAO Co-DETR model. Trigger phrases include "train Co-DETR", "run CoDINO", "codetr inference", "collaborative DETR", "autolabel detections with Co-DETR".AlteleFără avertismente★ 3,8 K↓ —
- mcore-run-on-slurmNVIDIA/skillsHow to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.AlteleFără avertismente★ 3,8 K↓ —
- tao-run-on-slurmNVIDIA/skillsRemote SLURM GPU cluster execution over SSH with sbatch/srun, Pyxis/Enroot containers, and Lustre-backed results. Use when running TAO training/eval/inference jobs on an on-prem or DGX SLURM cluster. Trigger phrases include "run on SLURM", "submit sbatch", "DGX SLURM cluster", "Pyxis/Enroot container", "Lustre dataset".AlteleFără avertismente★ 3,8 K↓ —
- nemo-relay-installNVIDIA/skillsUse this skill when choosing or running NeMo Relay installation for an agent runtime, application language, framework integration, or plugin bundle, or checking a runtime's native Relay readiness. Do not use it for first-run instrumentation or export configuration.AlteleFără avertismente★ 3,8 K↓ —
- nemo-retriever-mcpNVIDIA/skillsUse when a task needs to search or add documents through NeMo Retriever MCP.ProductivitateFără avertismente★ 3,8 K↓ —
- nemotron-retrieval-recipesNVIDIA/skillsUse when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.DevOpsFără avertismente★ 3,8 K↓ —
- image-enhanceZJU-REAL/Easel图片增强 / 放大 / 变清晰:高质量放大(Lanczos 2x/4x)+ 去噪 + 锐化 + 自动对比度/饱和度,改善偏糊、偏暗、噪点多的图片。当用户说 图片放大、图片变清晰、提高清晰度、图片增强、去噪点、锐化、图片太糊了、放大到高清、提升画质、优化图片、图片调亮调色 时使用。基于 shared/scripts/img_enhance.py(Pillow+OpenCV)。注意:这是传统增强非 AI 超分,凭空生成细节请用 ai-image-gen 图生图。与 image-editing 区别:那个做缩放/裁切/水印等常规操作,本 SKILL 专做画质提升。MediaFără avertismente★ 4,2 K↓ —
- style-transferZJU-REAL/Easel文案风格迁移:把一段文案从一种风格改写成另一种风格(严肃→搞笑、书面→口语、文艺→直白、正式→社交媒体感), 支持风格参考(给一段目标风格的示例文本)。 使用时机:用户说"改成搞笑风格"、"换个风格"、"风格迁移"、"改成口语化"、"改成文艺风"、 "像 XX 那样写"、"风格改写"、"换个腔调"。 和 text-polisher 的区别:polisher 提升质量不改风格,style-transfer 改变风格不做质量评估。AlteleFără avertismente★ 4,2 K↓ —
- skill-zhihu-answerZJU-REAL/Easel知乎问答回答发布:在知乎问题下发布原创回答——搜热门问题、检查可答性、写内容、 Playwright 发布(绕 header 遮挡 + JS 遍历发布按钮)。当用户说"回答知乎问题""在知乎问答下回答" "发知乎回答""知乎答题""帮我回答这个知乎问题""批量回答知乎"时使用。与 skill-zhihu-publisher 区别:那个发**专栏文章**(zhuanlan),本 SKILL 专发**问答区的回答**;回答内容写作用制作层 SKILL (novel-writer / copywriting)。TestareFără avertismente★ 4,2 K↓ —
gsd:code-review
davepoon/buildwithclaude
De la autor
Review source files changed during a phase for bugs, security issues, and code quality problems
Instalare
npx skills add davepoon/buildwithclaude --skill gsd:code-review -a claude-code
Se instalează în .claude/skills/gsd-code-review/ · 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.
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- SKILL.md