[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PMGPZVACZP8FWXT54RGMZM":3,"skill-same:01M6PMGPZVACZP8FWXT54RGMZM":124},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":14,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":6,"updatedAt":65,"install":66,"resources":79},"01M6PMGPZVACZP8FWXT54RGMZM","vss-setup-behavior-analytics",null,"Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fvss-setup-behavior-analytics\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"vss setup behavior analytics","setup","analytics","behavior","team","for","entrypoint","standalone","warehouse","summarization","Blueprint","use","github","Video","behavior-analytics","NVIDIA","https","search","deployment","config","2026-11-10 10:12:30",false,"---\nname: vss-setup-behavior-analytics\ndescription: Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.\nlicense: Apache-2.0\n\nmetadata:\n  author: \"NVIDIA Video Search and Summarization team\"\n  version: \"3.2.0\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization\"\n  tags: \"nvidia blueprint operational deployment behavior-analytics\"\n---\n## Purpose\n\nDeploy the behavior-analytics service standalone with the user's chosen entrypoint, config, and calibration.\n\n## Instructions\n\nFollow the routing tables and step-by-step workflows below. Each section that ends in *workflow*, *quick start*, or *flow* is intended to be executed top-to-bottom. Detailed reference material lives in `references\u002F`.\n\n## Examples\n\nWorked end-to-end examples are kept under `evals\u002F` (each `*.json` manifest\ncontains a runnable scenario). Run a Tier-3 evaluation to replay them:\n\n```bash\nnv-base validate skills\u002Fvss-setup-behavior-analytics --agent-eval\n```\n\nA minimal standalone bring-up looks like:\n\n```bash\ncd $REPO\u002Fdeploy\u002Fdocker\nexport VSS_APPS_DIR=$(pwd)\ndocker compose -f services\u002Fanalytics\u002Fbehavior-analytics\u002Fcompose.yml up -d vss-behavior-analytics-base\n```\n\nFollow `references\u002Fdeploy-behavior-analytics-service.md` for the full\nworkflow (entrypoint pick, config source, dynamic updates).\n\n## Limitations\n\n- Requires the matching VSS profile \u002F microservice to be deployed and reachable from the caller.\n- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.\n- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.\n\n## Troubleshooting\n\n- **Error**: REST call returns connection refused. **Cause**: target microservice not running. **Solution**: probe `\u002Fdocs` or `\u002Fhealth`; redeploy via `vss-deploy-profile` or the matching `vss-deploy-*` skill.\n- **Error**: HTTP 401\u002F403 from NGC pulls. **Cause**: missing\u002Fexpired `NGC_CLI_API_KEY`. **Solution**: `docker login nvcr.io` and re-export the key before retrying.\n- **Error**: container OOM or model fails to load. **Cause**: insufficient GPU memory for the selected profile. **Solution**: switch to a smaller variant or free GPUs via `docker compose down`.\n\n# VSS Setup Behavior Analytics — Standalone\n\nDeploy **just** the `vss-behavior-analytics` container (the spatial-AI analytics pipeline from the upstream `behavior-analytics` repo), not as part of the full warehouse blueprint stack.\n\nThe full operational walkthrough — entrypoint table, config-source options, calibration types, dynamic-update wire contract, troubleshooting — is [`references\u002Fdeploy-behavior-analytics-service.md`](references\u002Fdeploy-behavior-analytics-service.md). This SKILL.md only handles routing and prerequisites.\n\n## When to use\n\n- \"Deploy behavior analytics\" \u002F \"run behavior-analytics standalone\"\n- \"I just want to run analytics, not the full stack\"\n- \"Change the entrypoint to fusion_search \u002F dev_example \u002F analytics 3D \u002F mv3dt\"\n- \"Use my own behavior-analytics config \u002F calibration JSON\"\n- \"Point behavior-analytics at the warehouse-3d (or mv3dt) config without spinning up the rest of the warehouse profile\"\n- \"Dynamic config \u002F dynamic calibration into a running behavior-analytics\"\n\n## Prerequisites\n\n1. **Repo checkout** with `$VSS_APPS_DIR` pointing at `\u003Crepo>\u002Fdeploy\u002Fdocker\u002F`. Required by the service compose's volume binds.\n2. **NGC credentials** — `$NGC_CLI_API_KEY` set so docker can pull the image. See [`references\u002Fngc-api-key-registry-login.md`](references\u002Fngc-api-key-registry-login.md).\n3. **Docker runtime** — Docker Engine **28.3.3** with Docker Compose plugin **v2.39.1+**. Verify with `docker --version` and `docker compose version`.\n4. **Optional broker** (Kafka \u002F Redis Streams \u002F MQTT). The container starts fine **without** one — the Kafka client retries a bounded number of times, then the app exits and `restart: always` cycles the container. Status will show `Restarting (N)` in `docker ps` until a broker is reachable. With a broker, dynamic config \u002F dynamic calibration over `mdx-notification` become available.\n5. **Optional config \u002F calibration files on disk** if the user is bringing their own.\n\nIf any required prerequisite fails, surface the gap before going further.\n\n## Workflow\n\nHand the user [`references\u002Fdeploy-behavior-analytics-service.md`](references\u002Fdeploy-behavior-analytics-service.md) and walk them through its steps in order:\n\n1. Pick an entrypoint (analytics 2D \u002F 3D \u002F mv3dt, dev_example, fusion_search).\n2. Choose a config — profile-shipped or custom.\n3. Choose a calibration — optional; profile-shipped or custom; otherwise the app waits for a dynamic-calibration notification.\n4. Decide whether a broker is reachable; if yes, point them at the dynamic-update flows.\n\nThe compose-file edits, YAML diffs, deploy + verify commands, and troubleshooting table all live in that reference — don't duplicate them here.\n\n## Dynamic updates (runtime, no restart)\n\nOnce the container is up **and a broker is reachable**, two runtime-update flows are available — neither requires redeploying:\n\n### Dynamic config\n\nPublish an `upsert` (per-key patch) or `upsert-all` (full snapshot) message to the `mdx-notification` topic with Kafka key `behavior-analytics-config` and headers:\n\n- `event.type`: `upsert` | `upsert-all` | `request-config` | `ack`\n- `reference-id`: `video-analytics-api-\u003Cuuid>` (web-api originated), `behavior-analytics-\u003Cuuid>` (bootstrap reply), or the source-type literal (`kafka` \u002F `redis` \u002F `mqtt`) for direct-publisher upserts.\n\nBody: `{\"status\": ..., \"config\": \u003Cpatch>, \"error\": ...}`.\n\nThe listener validates each message at the envelope layer (rejects unknown keys, missing config, malformed status\u002Ferror) and at the per-payload layer (rejects forbidden sections, bad item shapes). Successful upserts are persisted to disk, applied to every worker, and ACK'd back over the topic.\n\nFull wire contract + ack semantics: [`references\u002Fdynamic-config.md`](references\u002Fdynamic-config.md).\n\n### Dynamic calibration\n\nPublish to the same topic with Kafka key `calibration` and headers:\n\n- `event.type`: `upsert-all` (full snapshot) | `upsert` (per-sensor merge) | `delete` (per-sensor removal)\n- `timestamp`: ISO-8601 UTC (`YYYY-MM-DDTHH:MM:SS.fffZ`).\n\nBody: JSON sensor list (and ROIs \u002F tripwires \u002F homographies for `upsert-all`).\n\nThe listener validates against the vendored AJV schema before persisting. Schema violations log a `calibration schema violation` warning and are dropped — the previously-good calibration stays loaded.\n\nFull wire contract + per-action validation policy: [`references\u002Fdynamic-calibration.md`](references\u002Fdynamic-calibration.md).\n\nBoth flows live entirely on the broker — the producer can be `video-analytics-api`, your own script, or any Kafka client that mirrors the wire shape. They're the recommended way to change configuration after the container is running, so the operator doesn't have to redeploy.\n\n## Routing rules\n\n- If the user wants \"the full stack\" (UI \u002F agent \u002F perception): hand off to [`vss-deploy-profile`](..\u002Fvss-deploy-profile\u002FSKILL.md) with profile `warehouse` (or `alerts`). Don't run this skill in parallel.\n- If the user wants to publish a runtime config \u002F calibration update to an already-running container: walk the [Dynamic updates](#dynamic-updates-runtime-no-restart) section. Both flows need a reachable broker.\n- If the user describes a behavior-analytics behavior change they want to validate (new incident type, new ROI rule, new sensor): point them at [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md), [`references\u002Fdynamic-config.md`](references\u002Fdynamic-config.md), or [`references\u002Fdynamic-calibration.md`](references\u002Fdynamic-calibration.md) before editing the JSON.\n\nbump:1\n","## Purpose\n\nDeploy the behavior-analytics service standalone with the user's chosen entrypoint, config, and calibration.\n\n## Instructions\n\nFollow the routing tables and step-by-step workflows below. Each section that ends in *workflow*, *quick start*, or *flow* is intended to be executed top-to-bottom. Detailed reference material lives in `references\u002F`.\n\n## Examples\n\nWorked end-to-end examples are kept under `evals\u002F` (each `*.json` manifest\ncontains a runnable scenario). Run a Tier-3 evaluation to replay them:\n\n```bash\nnv-base validate skills\u002Fvss-setup-behavior-analytics --agent-eval\n```\n\nA minimal standalone bring-up looks like:\n\n```bash\ncd $REPO\u002Fdeploy\u002Fdocker\nexport VSS_APPS_DIR=$(pwd)\ndocker compose -f services\u002Fanalytics\u002Fbehavior-analytics\u002Fcompose.yml up -d vss-behavior-analytics-base\n```\n\nFollow `references\u002Fdeploy-behavior-analytics-service.md` for the full\nworkflow (entrypoint pick, config source, dynamic updates).\n\n## Limitations\n\n- Requires the matching VSS profile \u002F microservice to be deployed and reachable from the caller.\n- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.\n- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.\n\n## Troubleshooting\n\n- **Error**: REST call returns connection refused. **Cause**: target microservice not running. **Solution**: probe `\u002Fdocs` or `\u002Fhealth`; redeploy via `vss-deploy-profile` or the matching `vss-deploy-*` skill.\n- **Error**: HTTP 401\u002F403 from NGC pulls. **Cause**: missing\u002Fexpired `NGC_CLI_API_KEY`. **Solution**: `docker login nvcr.io` and re-export the key before retrying.\n- **Error**: container OOM or model fails to load. **Cause**: insufficient GPU memory for the selected profile. **Solution**: switch to a smaller variant or free GPUs via `docker compose down`.\n\n# VSS Setup Behavior Analytics — Standalone\n\nDeploy **just** the `vss-behavior-analytics` container (the spatial-AI analytics pipeline from the upstream `behavior-analytics` repo), not as part of the full warehouse blueprint stack.\n\nThe full operational walkthrough — entrypoint table, config-source options, calibration types, dynamic-update wire contract, troubleshooting — is [`references\u002Fdeploy-behavior-analytics-service.md`](references\u002Fdeploy-behavior-analytics-service.md). This SKILL.md only handles routing and prerequisites.\n\n## When to use\n\n- \"Deploy behavior analytics\" \u002F \"run behavior-analytics standalone\"\n- \"I just want to run analytics, not the full stack\"\n- \"Change the entrypoint to fusion_search \u002F dev_example \u002F analytics 3D \u002F mv3dt\"\n- \"Use my own behavior-analytics config \u002F calibration JSON\"\n- \"Point behavior-analytics at the warehouse-3d (or mv3dt) config without spinning up the rest of the warehouse profile\"\n- \"Dynamic config \u002F dynamic calibration into a running behavior-analytics\"\n\n## Prerequisites\n\n1. **Repo checkout** with `$VSS_APPS_DIR` pointing at `\u003Crepo>\u002Fdeploy\u002Fdocker\u002F`. Required by the service compose's volume binds.\n2. **NGC credentials** — `$NGC_CLI_API_KEY` set so docker can pull the image. See [`references\u002Fngc-api-key-registry-login.md`](references\u002Fngc-api-key-registry-login.md).\n3. **Docker runtime** — Docker Engine **28.3.3** with Docker Compose plugin **v2.39.1+**. Verify with `docker --version` and `docker compose version`.\n4. **Optional broker** (Kafka \u002F Redis Streams \u002F MQTT). The container starts fine **without** one — the Kafka client retries a bounded number of times, then the app exits and `restart: always` cycles the container. Status will show `Restarting (N)` in `docker ps` until a broker is reachable. With a broker, dynamic config \u002F dynamic calibration over `mdx-notification` become available.\n5. **Optional config \u002F calibration files on disk** if the user is bringing their own.\n\nIf any required prerequisite fails, surface the gap before going further.\n\n## Workflow\n\nHand the user [`references\u002Fdeploy-behavior-analytics-service.md`](references\u002Fdeploy-behavior-analytics-service.md) and walk them through its steps in order:\n\n1. Pick an entrypoint (analytics 2D \u002F 3D \u002F mv3dt, dev_example, fusion_search).\n2. Choose a config — profile-shipped or custom.\n3. Choose a calibration — optional; profile-shipped or custom; otherwise the app waits for a dynamic-calibration notification.\n4. Decide whether a broker is reachable; if yes, point them at the dynamic-update flows.\n\nThe compose-file edits, YAML diffs, deploy + verify commands, and troubleshooting table all live in that reference — don't duplicate them here.\n\n## Dynamic updates (runtime, no restart)\n\nOnce the container is up **and a broker is reachable**, two runtime-update flows are available — neither requires redeploying:\n\n### Dynamic config\n\nPublish an `upsert` (per-key patch) or `upsert-all` (full snapshot) message to the `mdx-notification` topic with Kafka key `behavior-analytics-config` and headers:\n\n- `event.type`: `upsert` | `upsert-all` | `request-config` | `ack`\n- `reference-id`: `video-analytics-api-\u003Cuuid>` (web-api originated), `behavior-analytics-\u003Cuuid>` (bootstrap reply), or the source-type literal (`kafka` \u002F `redis` \u002F `mqtt`) for direct-publisher upserts.\n\nBody: `{\"status\": ..., \"config\": \u003Cpatch>, \"error\": ...}`.\n\nThe listener validates each message at the envelope layer (rejects unknown keys, missing config, malformed status\u002Ferror) and at the per-payload layer (rejects forbidden sections, bad item shapes). Successful upserts are persisted to disk, applied to every worker, and ACK'd back over the topic.\n\nFull wire contract + ack semantics: [`references\u002Fdynamic-config.md`](references\u002Fdynamic-config.md).\n\n### Dynamic calibration\n\nPublish to the same topic with Kafka key `calibration` and headers:\n\n- `event.type`: `upsert-all` (full snapshot) | `upsert` (per-sensor merge) | `delete` (per-sensor removal)\n- `timestamp`: ISO-8601 UTC (`YYYY-MM-DDTHH:MM:SS.fffZ`).\n\nBody: JSON sensor list (and ROIs \u002F tripwires \u002F homographies for `upsert-all`).\n\nThe listener validates against the vendored AJV schema before persisting. Schema violations log a `calibration schema violation` warning and are dropped — the previously-good calibration stays loaded.\n\nFull wire contract + per-action validation policy: [`references\u002Fdynamic-calibration.md`](references\u002Fdynamic-calibration.md).\n\nBoth flows live entirely on the broker — the producer can be `video-analytics-api`, your own script, or any Kafka client that mirrors the wire shape. They're the recommended way to change configuration after the container is running, so the operator doesn't have to redeploy.\n\n## Routing rules\n\n- If the user wants \"the full stack\" (UI \u002F agent \u002F perception): hand off to [`vss-deploy-profile`](..\u002Fvss-deploy-profile\u002FSKILL.md) with profile `warehouse` (or `alerts`). Don't run this skill in parallel.\n- If the user wants to publish a runtime config \u002F calibration update to an already-running container: walk the [Dynamic updates](#dynamic-updates-runtime-no-restart) section. Both flows need a reachable broker.\n- If the user describes a behavior-analytics behavior change they want to validate (new incident type, new ROI rule, new sensor): point them at [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md), [`references\u002Fdynamic-config.md`](references\u002Fdynamic-config.md), or [`references\u002Fdynamic-calibration.md`](references\u002Fdynamic-calibration.md) before editing the JSON.\n\nbump:1\n",{"name":5,"license":15,"metadata":54,"description":7},{"tags":55,"author":56,"version":57,"github-url":58},"nvidia blueprint operational deployment behavior-analytics","NVIDIA Video Search and Summarization 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Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[139,140,141,142,143,40,144,145,146,147,148],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":129,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":157,"publishedAt":49},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[155,156],{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},[158,159,141,160,161,162,163,164,40,165,166,167,168,169,144,145,170,171,34,172],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":129,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":183,"publishedAt":49},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[179,182],{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},[184,185,186,187,188,189,190,191,40,192,193,194,195,196,197,34,198],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":129,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":207,"publishedAt":49},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[205,206],{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},[208,141,209,160,210,211,212,213,143,40,214,168,144,145,215,216],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":129,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":230,"publishedAt":49},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[223,226,227],{"categoryNo":224,"categoryName":225,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":228,"categoryName":229,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师",[231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,145,249],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":251,"name":252,"nameZh":6,"description":253,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":129,"licenseFileUrl":6,"sourceHtmlUrl":254,"categories":255,"keywords":258,"publishedAt":49},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[256,257],{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":228,"categoryName":229,"taxonomyType":26,"taxonomyTypeDesc":27},[231,233,259,240,243,40,260,261,169,248,262,145,170,263,34,264,265],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]