[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CSXWPJ7V9GFS259MPB3ZDH":3,"skill-same:01M7CSXWPJ7V9GFS259MPB3ZDH":71},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":31,"repositoryHtmlUrl":32,"repositoryHomepageUrl":33,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M7CSXWPJ7V9GFS259MPB3ZDH","vss-ask-video",null,"Use to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: vss-ask-video\ndescription: Use to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions.\nlicense: Apache-2.0\nmetadata:\n  version: \"3.2.0\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization\"\n  tags: \"nvidia blueprint operational\"\n---\n\n# Video QnA using VLM through VSS Agent\n\nUse this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has **no** usable prior answer and needs a **fresh look at the pixels** for a specific clip.\n\n---\n\n## When to Use\n\n- The user asks **what happens in the video**, what **objects \u002F people \u002F actions** appear, **colors**, **timing**, **safety**, or other **visual facts** that require watching the clip.\n- The user asks for **details** that **cannot be answered** from existing messages, summaries, Elasticsearch\u002FMCP results, or filenames alone—you need **model inference on the video**.\n- Follow-up questions about **content details** after a coarse summary or after report generation.\n\nDo **not** use this skill when a **database \u002F MCP \u002F prior tool output** already answers the question, unless the user explicitly wants **verification** against the video.\n\n---\n\n## Deployment prerequisite\n\nThis skill requires a VSS profile that serves the `video_understanding` tool — typically **base** (recommended) or **lvs**. Before any request:\n\n1. Probe the VSS agent:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:8000\u002Fdocs\" >\u002Fdev\u002Fnull\n   ```\n\n2. **If the probe fails**, ask the user:\n   > *\"No VSS profile is running on `$HOST_IP`. Shall I deploy `base` (recommended for per-clip VLM QnA) using the `\u002Fvss-deploy-profile` skill? If you prefer `lvs`, say so.\"*\n\n   - If yes → hand off to `\u002Fvss-deploy-profile -p base` (or `-p lvs` if the user prefers). Return here once it succeeds.\n   - If no → stop.\n\n3. If the probe passes, proceed.\n\n---\n\n## Sensor prerequisite\n\n**You MUST list VST sensors before any `\u002Fgenerate` call.** This is required even when the user names the sensor explicitly, even when the user asserts the video is already uploaded, and even when a previous turn appeared to use the same video. Do not skip this step.\n\n1. List sensors:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fsensor\u002Flist\" | jq '.[].name'\n   ```\n\n2. Compare the returned `name` values against the user-supplied `\u003Csensor-id>` (or **filename stem**, e.g. `warehouse_safety_0001`).\n\n3. **If a matching sensor is present** → proceed to the Agent workflow below.\n\n4. **If no matching sensor is present** — upload the video first, then re-list to confirm the new sensor appears:\n   ```bash\n   # filename: must not contain whitespace\n   # timestamp: ISO 8601 UTC — default 2025-01-01T00:00:00.000Z if user did not specify\n   curl -s -X PUT \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fstorage\u002Ffile\u002F\u003Cfilename>?timestamp=\u003Ctimestamp>\" \\\n     -H \"Content-Type: application\u002Foctet-stream\" \\\n     -H \"Content-Length: \u003Cfile_size_in_bytes>\" \\\n     --upload-file \u002Fpath\u002Fto\u002F\u003Cfilename> | jq .\n   ```\n   See `\u002Fvss-manage-video-io-storage` for full upload semantics (v1 vs v2, conflict handling, delete flow). In interactive runs, confirm with the user before uploading. **Never** issue an unconditional PUT without first running the sensor-list check above — that is exactly the failure mode this prerequisite exists to prevent.\n\n---\n\n## Agent workflow\n\nThe Sensor prerequisite above must have already confirmed (or made) the sensor exist on VST. Then:\n\n1. **Clip** — Identify **sensor id**, **filename**, or **URL** for one video segment. If ambiguous, ask the user.\n2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question.\n3. Return the vss agent's answer back to the user.\n\n\n## Query VSS agent (`\u002Fgenerate`)\n\n```bash\n# Set from deployment (compose \u002F .env \u002F host where vss-agent listens)\nexport VSS_AGENT_BASE_URL=\"http:\u002F\u002Flocalhost:8000\"\n\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\": \"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' | jq .\n```\n\n---\n\n## Cross-Reference\n\n- **vss-manage-video-io-storage** — VST storage\u002Freplay URLs so **`VIDEO_URL`** is valid for the VLM.\n- **vss-generate-video-report** — timestamped **reports** via the **VSS agent** (`\u002Fgenerate`); this skill is **direct VLM** for ad-hoc **video Q&A**.\n\n\n","# Video QnA using VLM through VSS Agent\n\nUse this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has **no** usable prior answer and needs a **fresh look at the pixels** for a specific clip.\n\n---\n\n## When to Use\n\n- The user asks **what happens in the video**, what **objects \u002F people \u002F actions** appear, **colors**, **timing**, **safety**, or other **visual facts** that require watching the clip.\n- The user asks for **details** that **cannot be answered** from existing messages, summaries, Elasticsearch\u002FMCP results, or filenames alone—you need **model inference on the video**.\n- Follow-up questions about **content details** after a coarse summary or after report generation.\n\nDo **not** use this skill when a **database \u002F MCP \u002F prior tool output** already answers the question, unless the user explicitly wants **verification** against the video.\n\n---\n\n## Deployment prerequisite\n\nThis skill requires a VSS profile that serves the `video_understanding` tool — typically **base** (recommended) or **lvs**. Before any request:\n\n1. Probe the VSS agent:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:8000\u002Fdocs\" >\u002Fdev\u002Fnull\n   ```\n\n2. **If the probe fails**, ask the user:\n   > *\"No VSS profile is running on `$HOST_IP`. Shall I deploy `base` (recommended for per-clip VLM QnA) using the `\u002Fvss-deploy-profile` skill? If you prefer `lvs`, say so.\"*\n\n   - If yes → hand off to `\u002Fvss-deploy-profile -p base` (or `-p lvs` if the user prefers). Return here once it succeeds.\n   - If no → stop.\n\n3. If the probe passes, proceed.\n\n---\n\n## Sensor prerequisite\n\n**You MUST list VST sensors before any `\u002Fgenerate` call.** This is required even when the user names the sensor explicitly, even when the user asserts the video is already uploaded, and even when a previous turn appeared to use the same video. Do not skip this step.\n\n1. List sensors:\n   ```bash\n   curl -sf --max-time 5 \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fsensor\u002Flist\" | jq '.[].name'\n   ```\n\n2. Compare the returned `name` values against the user-supplied `\u003Csensor-id>` (or **filename stem**, e.g. `warehouse_safety_0001`).\n\n3. **If a matching sensor is present** → proceed to the Agent workflow below.\n\n4. **If no matching sensor is present** — upload the video first, then re-list to confirm the new sensor appears:\n   ```bash\n   # filename: must not contain whitespace\n   # timestamp: ISO 8601 UTC — default 2025-01-01T00:00:00.000Z if user did not specify\n   curl -s -X PUT \"http:\u002F\u002F${HOST_IP}:30888\u002Fvst\u002Fapi\u002Fv1\u002Fstorage\u002Ffile\u002F\u003Cfilename>?timestamp=\u003Ctimestamp>\" \\\n     -H \"Content-Type: application\u002Foctet-stream\" \\\n     -H \"Content-Length: \u003Cfile_size_in_bytes>\" \\\n     --upload-file \u002Fpath\u002Fto\u002F\u003Cfilename> | jq .\n   ```\n   See `\u002Fvss-manage-video-io-storage` for full upload semantics (v1 vs v2, conflict handling, delete flow). In interactive runs, confirm with the user before uploading. **Never** issue an unconditional PUT without first running the sensor-list check above — that is exactly the failure mode this prerequisite exists to prevent.\n\n---\n\n## Agent workflow\n\nThe Sensor prerequisite above must have already confirmed (or made) the sensor exist on VST. Then:\n\n1. **Clip** — Identify **sensor id**, **filename**, or **URL** for one video segment. If ambiguous, ask the user.\n2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question.\n3. Return the vss agent's answer back to the user.\n\n\n## Query VSS agent (`\u002Fgenerate`)\n\n```bash\n# Set from deployment (compose \u002F .env \u002F host where vss-agent listens)\nexport VSS_AGENT_BASE_URL=\"http:\u002F\u002Flocalhost:8000\"\n\ncurl -s -X POST \"${VSS_AGENT_BASE_URL}\u002Fgenerate\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"input_message\": \"Call video_understanding tool to answer the following question about \u003Csensor-id>: \u003Cuser query>\"}' | jq .\n```\n\n---\n\n## Cross-Reference\n\n- **vss-manage-video-io-storage** — VST storage\u002Freplay URLs so **`VIDEO_URL`** is valid for the VLM.\n- **vss-generate-video-report** — timestamped **reports** via the **VSS agent** (`\u002Fgenerate`); this skill is **direct VLM** for ad-hoc **video Q&A**.\n\n\n",{"name":5,"license":15,"metadata":24,"description":7},{"tags":25,"version":26,"github-url":27},"nvidia blueprint operational","3.2.0","https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization","main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill vss-ask-video",{"tool":43,"command":44},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill vss-ask-video",{"tool":46,"command":47},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill vss-ask-video","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002FSKILL.md",true,[51,57,61,65],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"BENCHMARK.md","REFERENCE","参考文档",2756,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002FBENCHMARK.md",{"path":58,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":59,"sourceHtmlUrl":60},"evals\u002Fbase_profile_video_understanding.json",3328,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002Fevals\u002Fbase_profile_video_understanding.json",{"path":62,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":63,"sourceHtmlUrl":64},"skill-card.md",2808,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002Fskill-card.md",{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"skill.oms.sig","OTHER","其他",4617,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-skills\u002Fskills\u002Fvss-ask-video\u002Fskill.oms.sig",[72,80,87,94,101,108],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":76,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]