Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
45,604 skills from 2,205 repositories · updated 19 hours ago
44,573 skills
- skill-trend-riderZJU-REAL/Easel给定一个热点事件或话题，结合创作者账号定位，输出蹭热点的具体内容方案。 包含切入角度、内容形式、标题建议、风险提醒。 当用户说"这个热点怎么蹭"、"蹭热点"、"怎么结合"、"热点和我有关吗"、 "追热点"、"热点方案"、"借势"、"怎么追这个热点"、"热点怎么切"时触发。 和 skill-trending-topics 的区别：trending-topics 发现和筛选热点， trend-rider 拿到一个具体热点后规划怎么蹭、怎么切内容。OtherNo warnings★ 4.1K↓ —
- video-intro-outroZJU-REAL/Easel视频片头 / 片尾：生成带标题、副标题、logo、关注引导的片头卡片和片尾卡片，并拼接到主视频（硬切或淡入淡出转场）。当用户说 片头、片尾、加片头片尾、开场卡片、结尾卡片、标题卡、关注引导页、订阅引导、视频开头加标题、结尾加点赞关注、intro、outro、片头动画 时使用。基于 shared/scripts/intro_outro.py 确定性 ffmpeg 封装。与 poster-hero 区别：poster-hero 出静态封面图，本 SKILL 出可拼接的视频卡片片段。MediaNo warnings★ 4.1K↓ —
- doca-staNVIDIA/skillsUse this skill when the user builds an NVMe-over-Fabrics storage target on BlueField with DOCA STA — instantiating doca_sta, declaring doca_sta_subsystem NQNs and namespaces over local NVMe disks via doca_sta_be, validating platform support with doca_sta_cap_is_supported, sizing I/O queue depth, or decoding DOCA_ERROR_* statuses such as IO_FAILED on NVMe reads. Also load it for implied asks like "Identify Controller times out over RoCE", "offload the nvmf target", "NVMe-oF Connect never completes", or "16 I/O queues at depth 1024 — supported?". Refuse and route to the proper owner for raw RDMA movement, packet I/O, steering rules, initiator-side NVMe, or SDK install.OtherNo warnings★ 3.7K↓ —
- doca-argpNVIDIA/skillsUse this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from the full public enum (`DOCA_ARGP_TYPE_STRING`, `_INT`, `_BOOLEAN`, `_DEVICE`, `_DEVICE_REP`, `_DOUBLE` — six values, not three); preserving the built-in DOCA flags (`-h` `-v` `-l` `--sdk-log-level` `--log-filter` `-j`/`--json`, NOT `--json-config`); `--device` is per-program; or debugging `DOCA_ERROR_BAD_STATE` / `INVALID_VALUE` / `NOT_SUPPORTED` / `IO_FAILED` from `doca_argp_*`. Trigger on implicit phrasings: "add a custom flag to a DOCA sample", "should I use getopt here", "BAD_STATE registering a new param", "my JSON config key is rejected", or "my sample's --json is ignored". Refuse and route elsewhere for variadic-flag / subcommand / shell-completion features, DOCA Core context, or DOCA Log internals.EngineeringNo warnings★ 3.7K↓ —
- video-to-articleZJU-REAL/Easel把口播、讲座、直播或 Vlog 转录并改写成小红书笔记、公众号文章或知乎内容，同时抽帧配图。当用户说“视频转图文/文章/笔记、视频扒文案、口播转文章、视频内容复用”时使用。只生成字幕文件用 auto-subtitle；翻译已有字幕用 subtitle-translate。MediaNo warnings★ 4.1K↓ —
- doca-capsNVIDIA/skillsUse this skill when the user wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for debug or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this box", "is my BlueField PF visible to DOCA", "is Flow available on my RHEL host", "enumerate VF representors for pf0", "doca_caps: command not found", or "empty output for RDMA, is the tool broken". Refuse and route elsewhere for DOCA installation (doca-setup owns it; never walk the steps), library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary.TestingNo warnings★ 3.7K↓ —
- coda-automationdavepoon/buildwithclaudeAutomate Coda tasks via Rube MCP (Composio): manage docs, pages, tables, rows, formulas, permissions, and publishing. Always search tools first for current schemas.ProductivityNo warnings★ 3.7K↓ —
- subtitle-translateZJU-REAL/Easel字幕翻译 / 双语字幕：把已有字幕（SRT/VTT/ASS）翻译成目标语言，生成双语（原文+译文）或纯译文字幕，并可软挂载 / 硬烧录进视频。当用户说 字幕翻译、翻译字幕、双语字幕、中英字幕、给视频加翻译、SRT 翻译、字幕本地化、把字幕翻成中文/英文、字幕转双语 时使用。翻译由 LLM 完成，时间轴/格式/烧录由 shared/scripts/subtitle_ops.py 确定性处理。与 auto-subtitle 区别：auto-subtitle 是语音识别生成字幕，本 SKILL 是把已有字幕翻译成双语。OtherNo warnings★ 4.1K↓ —
- video-highlightsZJU-REAL/Easel长视频 / 直播录像高光切片：从一条长视频里找出高光片段，切成多条独立短视频，可选转竖版 9:16 + 加字幕。找点两种方式——音频能量峰值（情绪高涨/欢呼/大声处）或转录后由内容判断挑金句段。当用户说 直播切片、高光切片、长视频切短、录像剪精华、切片、高光时刻、把直播剪成短视频、提取精彩片段、长视频找亮点 时使用。基于 shared/scripts/highlight_cut.py（librosa 能量分析）。与 clipify 区别：clipify 专做英文口播找笑点+动态人脸pan，本 SKILL 更通用（中文/直播皆可）、静态转竖版更稳。MediaNo warnings★ 4.1K↓ —
- chart-visualizationZJU-REAL/Easel将数据可视化为图表。当用户需要生成柱状图、折线图、饼图、散点图、雷达图、桑基图、思维导图、流程图等图表时调用此技能，通过 curl 工具调用 AntV API 生成图表图片。产出静态图片 URL（25+ 类型）；要本地渲染的信息图/GIF 动画图表用 infographic，要 CSV/JSON→整页报告用 data-reportDataNo warnings★ 4.1K↓ —
- skill-livestreamZJU-REAL/Easel为直播生成完整方案：直播主题、流程时间表、开场白/过渡语/催单话术/感谢话术/互动话术。 适用于带货直播、知识分享直播、娱乐直播。 当用户说"直播策划"、"直播方案"、"直播话术"、"开播前准备"、"直播流程"、 "直播脚本"、"带货直播"、"怎么开播"、"直播怎么做"、"直播排期"、"写个直播话术"时触发。OtherNo warnings★ 4.1K↓ —
- skill-my-accountZJU-REAL/Easel查询用户自己在 Easel 已登录的小红书、抖音、快手、知乎和视频号身份、粉丝、获赞、关注及作品列表。 当用户问“我登录了哪些号、我是谁、我的粉丝/获赞、我最近发了什么、我有哪些帖子”时，先用本 SKILL 查本地登录态，不要先向用户索要账号名或链接。 某条帖子的评论抓取与分析分别用 skill-xhs-comment-reply 和 skill-comment-insights。OtherNo warnings★ 4.1K↓ —
- agent-analyticsdavepoon/buildwithclaudeAnalytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.DataNo warnings★ 3.7K↓ —
- doca-flowNVIDIA/skillsDOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Remote or non-C++ control over gRPC belongs to doca-flow-grpc-server. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.EngineeringNo warnings★ 3.7K↓ —
- comparison-cardZJU-REAL/Easel对比图/一图流：生成 A vs B 参数对比图、优劣势对比表、产品参数一图流。 用 HTML+CSS 渲染成可截图的视觉卡片，适合小红书/微博等平台分享。 使用时机：用户说"做个对比图"、"A vs B"、"参数对比"、"优劣对比"、 "一图流"、"对比表"、"哪个好"、"对比一下"。 和 chart-visualization 的区别：chart 做数据图表（柱状图/折线图），comparison-card 做对比表/一图流。 和 infographic 的区别：infographic 做多维信息图，comparison-card 专注 A vs B 对比。DesignNo warnings★ 4.1K↓ —
- tao-train-segformerNVIDIA/skillsSegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running inference for a TAO SegFormer model. Trigger phrases include "train SegFormer", "semantic segmentation", "lightweight transformer segmenter", "real-time semantic segmentation".PlanningNo warnings★ 3.7K↓ —
- beat-sync-videoZJU-REAL/Easel音乐卡点视频 / 踩点视频：检测背景音乐的节拍，让图片或片段在节拍点上切换，配推进/白闪特效，做出燃系'卡点'短视频。当用户说 卡点视频、踩点视频、音乐卡点、节奏卡点、按音乐切换、鼓点视频、踩节奏、beat 卡点、图片卡点、卡点混剪 时使用。基于 shared/scripts/beatsync.py（librosa 节拍检测）。与 slideshow-video 区别：slideshow 每图固定时长、柔和转场，本 SKILL 切换点由音乐节拍决定、硬切踩点带特效。MediaNo warnings★ 4.1K↓ —
- 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。OtherNo warnings★ 4.1K↓ —
- text-condenserZJU-REAL/Easel字数裁剪/摘要：把长文本压缩到指定字数，保留核心信息。支持硬裁剪（严格字数）、 摘要（保留要点）、金句提取（只保留最精华的句子）三种模式。 特别适合从长文生成平台适配的短文。 使用时机：用户说"裁到 140 字"、"压缩一下"、"太长了"、"摘要"、"精简"、 "缩写"、"字数裁剪"、"帮我缩短"、"提炼要点"。 和 text-polisher 的区别：polisher 润色文案不改长度，condenser 压缩篇幅不做质量提升。OtherNo warnings★ 4.1K↓ —
- slideshow-videoZJU-REAL/Easel图片相册 → 视频：把一组图片做成带 Ken Burns 缓慢缩放、图间转场、背景音乐和逐图字幕的视频，自动适配平台画幅（竖版/方形/横版）。当用户说 图片转视频、图片做成视频、相册视频、照片视频、一组图生成视频、图片轮播视频、幻灯片视频、把这几张图做成短视频、图集成片、卡点图片视频 时使用。基于 shared/scripts/slideshow.py 确定性 ffmpeg 封装。与 auto-short-video 区别：auto-short-video 从一句话主题自动生成配图/配音成片，本 SKILL 用用户已有的图片直接拼成视频。MediaNo warnings★ 4.1K↓ —
- doca-spcx-ccNVIDIA/skillsUse this skill when the user is invoking `doca_spcx_cc` (the host-side CLI under /opt/mellanox/doca/tools/) to load, parameterize, start, observe, or stop a Programmable Congestion Control (SPCX) algorithm on a BlueField with a DPA processor against a live RDMA / RoCE fabric, or picking SPCX vs the established `doca-pcc` surface. Trigger even when the user does not say "DOCA SPCX" or "doca_spcx_cc" — typical implicit phrasings include "I want to write a custom RTT-based CC algorithm for my RoCE fabric", "my SPCX session loaded but throughput / latency didn't change", "doca_pcc status shows Active but factory CC seems to still be in charge", "DOCA_PCC_PS_ERROR on start", "is the programmable-CC surface available on my install", or "DPA-side algorithm image won't load". Refuse and route elsewhere for DPA-side algorithm authoring detail, factory PCC firmware configuration, read-only PCC counter inspection, raw DPA cycle profiling, RDMA library programming, or general DOCA install — those belong to other skills.MediaNo warnings★ 3.7K↓ —
- skill-douyin-uploadZJU-REAL/Easel将视频/图文内容发布到抖音（creator.douyin.com）。基于 Playwright + 持久化登录态，headless 即可运行， 流程与选择器移植自开源实现 douyin-upload-mcp-skill（含高清发布入口、切 tab、上传等转码、AI 封面、 发布成功 toast 校验、二维码登录）。适用场景：发布抖音视频、发布图文、扫码登录、发布前预检。TestingNo warnings★ 4.1K↓ —
- skill-content-gap-analysisZJU-REAL/Easel分析社媒赛道的内容空白，发现高需求低竞争的蓝海选题机会。 当用户说"蓝海选题""内容空白""没人做的选题""选题机会""高需求低竞争""差异化选题""内容缺口"时使用。 和 skill-competitor-analysis 的区别：本 SKILL 从赛道供需交叉验证找蓝海选题空白； competitor-analysis 拆解具体竞品账号的内容策略。OtherNo warnings★ 4.1K↓ —
- gsd:inboxdavepoon/buildwithclaudeTriage and review all open GitHub issues and PRs against project templates and contribution guidelinesEngineeringNo warnings★ 3.7K↓ —
tao-train-mask-auto-label
NVIDIA/skills
OtherUpdated 2 days ago
From the author
MAL (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".
Install
npx skills add NVIDIA/skills --skill tao-train-mask-auto-label -a claude-code
Installs to .claude/skills/tao-train-mask-auto-label/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
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Instructions onlyNo network use seenLicence: APACHE-2.0
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Why 90
Security100
Provenance83
Maintenance100
Licence100
Adoption57
Files (14)
- BENCHMARK.md
- SKILL.md
- config/skillspector-baseline.yaml
- evals/evals.json
- references/skill_info.yaml
- references/spec_template_evaluate.yaml
- references/spec_template_inference.yaml
- references/spec_template_train.yaml
- schemas/evaluate.schema.json
- schemas/inference.schema.json
- schemas/manifest.json
- schemas/train.schema.json
- +2 more