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Not for 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.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002FSKILL.md",[18,23,26,29,34],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":35,"categoryName":36,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000033","数据分析师",[38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],"Video","vss setup video analytics api","setup","api","analytics","kafka","NVIDIA","REST","elasticsearch","rest-api","search","deployment","team","use","log","Blueprint","bind","warehouse","standalone","for","2026-11-10 10:12:30",false,"---\nname: vss-setup-video-analytics-api\ndescription: Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.\nlicense: Apache-2.0\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 video-analytics-api rest-api\"\n---\n## Purpose\n\nDeploy the video-analytics-api REST service standalone with the user's chosen config, data-log bind, and Elasticsearch \u002F Kafka connectivity.\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-video-analytics-api --agent-eval\n```\n\nA minimal standalone bring-up looks like:\n\n```bash\ncd $REPO\u002Fdeploy\u002Fdocker\nexport VSS_APPS_DIR=$(pwd)\nexport VSS_DATA_DIR=${VSS_DATA_DIR:-\u002Ftmp\u002Fvss-data}\nmkdir -p \"$VSS_DATA_DIR\u002Fdata_log\u002Fvss_video_analytics_api\"\ndocker compose -f services\u002Fanalytics\u002Fvideo-analytics-api\u002Fcompose.yml up -d vss-video-analytics-api\ncurl -sf http:\u002F\u002Flocalhost:8081\u002Flivez\n```\n\nFollow [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) for the full\nworkflow (config source, data-log bind, infrastructure dependencies, REST endpoints).\nFor the field-by-field JSON config reference, see [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md).\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 Video Analytics API — Standalone\n\nDeploy **just** the `vss-video-analytics-api` container (the Node.js REST API from the upstream `video-analytics-api` repo), not as part of the full warehouse blueprint stack.\n\nThe full operational walkthrough — config-source options, data-log volume behavior, infrastructure dependencies, REST API endpoints, deploy + verify, troubleshooting — lives in [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md). The field-by-field JSON config reference lives in [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md). This SKILL.md only handles routing and prerequisites.\n\n## When to use\n\n- \"Deploy video analytics api\" \u002F \"run video-analytics-api standalone\"\n- \"I just want to run the REST API, not the full stack\"\n- \"Use my own video-analytics-api config\"\n- \"Point the API at a different Elasticsearch \u002F Kafka\"\n- \"Start the API without Kafka\" \u002F \"run the API broker-less\"\n- \"Check what REST endpoints are available\"\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).\n\n   > **Secure-handling note for `NGC_CLI_API_KEY`**: this key is a\n   > long-lived credential that pulls all NVIDIA private images\n   > available to your NGC org. Never commit the key, never paste it\n   > into chat, never store it in `\u002Ftmp`. Read it interactively\n   > (`read -rs NGC_CLI_API_KEY`) or load it from your secret manager\n   > (Vault, AWS Secrets Manager, sealed-secrets) at deploy time.\n   > Write any derived `.env` files with `umask 077` + `chmod 600`,\n   > add them to `.gitignore`, and rotate the key on a defined\n   > cadence and after every host decommission. If it has ever been\n   > exposed (host snapshot, shared screen, ticket attachment),\n   > rotate immediately.\n3. **Docker runtime** — Docker Engine **28.3.3** with Docker Compose plugin **v2.39.1+**. Verify with `docker --version` and `docker compose version`.\n4. **Elasticsearch** — must be reachable at the URL configured in `elasticsearch.node`. The server pings ES on startup; if unreachable, it exits (and `restart: always` brings it back). If you need to bring up ES too, use the infra compose: `docker compose -f services\u002Finfra\u002Fcompose.yml up -d elasticsearch`.\n5. **Optional Kafka broker**. The API can run without Kafka. If you want a quiet broker-less deployment, use the image-baked config or a custom config with `kafka.brokers: []`; the service-shipped compose config points at `localhost:9092`, so Kafka-dependent features (dynamic config, dynamic calibration, RTLS\u002FAMR) will fail until a broker is reachable.\n6. **`$VSS_DATA_DIR` for the default compose.** The base compose bind-mounts `$VSS_DATA_DIR\u002Fdata_log\u002Fvss_video_analytics_api` for multipart upload handling and file-backed assets such as calibration images. Set the directory to a writable host path and pre-create it, or remove that mount if image uploads are not needed.\n\nIf any required prerequisite fails, surface the gap before going further.\n\n## Workflow\n\nHand the user [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) and walk them through its steps in order:\n\n1. Choose a config — image-baked default, service-shipped, or custom.\n2. Decide whether a data-log volume is needed for file uploads.\n3. Confirm infrastructure dependencies — Elasticsearch (required), Kafka (optional).\n4. Deploy + verify with `docker compose up` and health check.\n\nThe compose-file edits, config options, deploy + verify commands, REST API endpoint table, and troubleshooting table all live in that reference — don't duplicate them here.\n\n## Endpoint Reference\n\nUse [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) for the REST endpoint table and runtime dependency notes.\n\n## Kafka-dependent features (runtime, requires broker)\n\nOnce the container is up **and a Kafka broker is reachable**, three additional capabilities are available:\n\n### Dynamic config\n\nThe API acts as the **producer** for dynamic config updates. When an operator POSTs to `\u002Fconfig`, the API publishes an `upsert` message to the `mdx-notification` topic with Kafka key `behavior-analytics-config`. The downstream `behavior-analytics` container consumes this and ACKs back. The API also handles the bootstrap flow — when `behavior-analytics` starts, it publishes a `request-config` message, and the API replies with `upsert-all` containing the latest verified config from Elasticsearch.\n\nConsumer-side validation, ACK semantics, and the full wire contract are documented in the `vss-setup-behavior-analytics` dynamic-config reference.\n\n### Dynamic calibration\n\nThe API produces calibration update notifications on `mdx-notification` with Kafka key `calibration`. Supports `upsert-all` (full snapshot), `upsert` (per-sensor merge), and `delete` (per-sensor removal). The downstream `behavior-analytics` container consumes these and applies them to the live calibration.\n\nConsumer-side validation and per-action policy are documented in the `vss-setup-behavior-analytics` dynamic-calibration reference.\n\n### RTLS \u002F AMR\n\nThe API consumes real-time location (`mdx-rtls`) and AMR (`mdx-amr`) messages from Kafka and exposes them via REST endpoints.\n\n## Routing rules\n\n- If the user wants \"the full stack\" (UI \u002F agent \u002F perception): hand off to `vss-deploy-profile` with profile `warehouse` (or `alerts`). Don't run this skill in parallel.\n- If the user wants to deploy the analytics pipeline (behavior creation, incident detection): hand off to `vss-setup-behavior-analytics`.\n- If the user wants to publish a runtime config \u002F calibration update through the REST API: confirm Kafka is reachable, then use the `\u002Fconfig` or calibration endpoints and point them at the behavior-analytics dynamic-update references for the consumer wire contract.\n- If the user wants to understand the dynamic config \u002F dynamic calibration wire contract from the **consumer** (behavior-analytics) side: point them at the `vss-setup-behavior-analytics` dynamic-config and dynamic-calibration references.\n- If the user wants to query or interact with the REST API endpoints: the deploy reference endpoint table covers what's available. For the full OpenAPI spec, see `src\u002Fapp\u002Fspecification\u002Fopenapi.json` in the `video-analytics-api` repo.\n\n\nbump:1\n","## Purpose\n\nDeploy the video-analytics-api REST service standalone with the user's chosen config, data-log bind, and Elasticsearch \u002F Kafka connectivity.\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-video-analytics-api --agent-eval\n```\n\nA minimal standalone bring-up looks like:\n\n```bash\ncd $REPO\u002Fdeploy\u002Fdocker\nexport VSS_APPS_DIR=$(pwd)\nexport VSS_DATA_DIR=${VSS_DATA_DIR:-\u002Ftmp\u002Fvss-data}\nmkdir -p \"$VSS_DATA_DIR\u002Fdata_log\u002Fvss_video_analytics_api\"\ndocker compose -f services\u002Fanalytics\u002Fvideo-analytics-api\u002Fcompose.yml up -d vss-video-analytics-api\ncurl -sf http:\u002F\u002Flocalhost:8081\u002Flivez\n```\n\nFollow [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) for the full\nworkflow (config source, data-log bind, infrastructure dependencies, REST endpoints).\nFor the field-by-field JSON config reference, see [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md).\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 Video Analytics API — Standalone\n\nDeploy **just** the `vss-video-analytics-api` container (the Node.js REST API from the upstream `video-analytics-api` repo), not as part of the full warehouse blueprint stack.\n\nThe full operational walkthrough — config-source options, data-log volume behavior, infrastructure dependencies, REST API endpoints, deploy + verify, troubleshooting — lives in [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md). The field-by-field JSON config reference lives in [`references\u002Fconfiguration.md`](references\u002Fconfiguration.md). This SKILL.md only handles routing and prerequisites.\n\n## When to use\n\n- \"Deploy video analytics api\" \u002F \"run video-analytics-api standalone\"\n- \"I just want to run the REST API, not the full stack\"\n- \"Use my own video-analytics-api config\"\n- \"Point the API at a different Elasticsearch \u002F Kafka\"\n- \"Start the API without Kafka\" \u002F \"run the API broker-less\"\n- \"Check what REST endpoints are available\"\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).\n\n   > **Secure-handling note for `NGC_CLI_API_KEY`**: this key is a\n   > long-lived credential that pulls all NVIDIA private images\n   > available to your NGC org. Never commit the key, never paste it\n   > into chat, never store it in `\u002Ftmp`. Read it interactively\n   > (`read -rs NGC_CLI_API_KEY`) or load it from your secret manager\n   > (Vault, AWS Secrets Manager, sealed-secrets) at deploy time.\n   > Write any derived `.env` files with `umask 077` + `chmod 600`,\n   > add them to `.gitignore`, and rotate the key on a defined\n   > cadence and after every host decommission. If it has ever been\n   > exposed (host snapshot, shared screen, ticket attachment),\n   > rotate immediately.\n3. **Docker runtime** — Docker Engine **28.3.3** with Docker Compose plugin **v2.39.1+**. Verify with `docker --version` and `docker compose version`.\n4. **Elasticsearch** — must be reachable at the URL configured in `elasticsearch.node`. The server pings ES on startup; if unreachable, it exits (and `restart: always` brings it back). If you need to bring up ES too, use the infra compose: `docker compose -f services\u002Finfra\u002Fcompose.yml up -d elasticsearch`.\n5. **Optional Kafka broker**. The API can run without Kafka. If you want a quiet broker-less deployment, use the image-baked config or a custom config with `kafka.brokers: []`; the service-shipped compose config points at `localhost:9092`, so Kafka-dependent features (dynamic config, dynamic calibration, RTLS\u002FAMR) will fail until a broker is reachable.\n6. **`$VSS_DATA_DIR` for the default compose.** The base compose bind-mounts `$VSS_DATA_DIR\u002Fdata_log\u002Fvss_video_analytics_api` for multipart upload handling and file-backed assets such as calibration images. Set the directory to a writable host path and pre-create it, or remove that mount if image uploads are not needed.\n\nIf any required prerequisite fails, surface the gap before going further.\n\n## Workflow\n\nHand the user [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) and walk them through its steps in order:\n\n1. Choose a config — image-baked default, service-shipped, or custom.\n2. Decide whether a data-log volume is needed for file uploads.\n3. Confirm infrastructure dependencies — Elasticsearch (required), Kafka (optional).\n4. Deploy + verify with `docker compose up` and health check.\n\nThe compose-file edits, config options, deploy + verify commands, REST API endpoint table, and troubleshooting table all live in that reference — don't duplicate them here.\n\n## Endpoint Reference\n\nUse [`references\u002Fdeploy-video-analytics-api-service.md`](references\u002Fdeploy-video-analytics-api-service.md) for the REST endpoint table and runtime dependency notes.\n\n## Kafka-dependent features (runtime, requires broker)\n\nOnce the container is up **and a Kafka broker is reachable**, three additional capabilities are available:\n\n### Dynamic config\n\nThe API acts as the **producer** for dynamic config updates. When an operator POSTs to `\u002Fconfig`, the API publishes an `upsert` message to the `mdx-notification` topic with Kafka key `behavior-analytics-config`. The downstream `behavior-analytics` container consumes this and ACKs back. The API also handles the bootstrap flow — when `behavior-analytics` starts, it publishes a `request-config` message, and the API replies with `upsert-all` containing the latest verified config from Elasticsearch.\n\nConsumer-side validation, ACK semantics, and the full wire contract are documented in the `vss-setup-behavior-analytics` dynamic-config reference.\n\n### Dynamic calibration\n\nThe API produces calibration update notifications on `mdx-notification` with Kafka key `calibration`. Supports `upsert-all` (full snapshot), `upsert` (per-sensor merge), and `delete` (per-sensor removal). The downstream `behavior-analytics` container consumes these and applies them to the live calibration.\n\nConsumer-side validation and per-action policy are documented in the `vss-setup-behavior-analytics` dynamic-calibration reference.\n\n### RTLS \u002F AMR\n\nThe API consumes real-time location (`mdx-rtls`) and AMR (`mdx-amr`) messages from Kafka and exposes them via REST endpoints.\n\n## Routing rules\n\n- If the user wants \"the full stack\" (UI \u002F agent \u002F perception): hand off to `vss-deploy-profile` with profile `warehouse` (or `alerts`). Don't run this skill in parallel.\n- If the user wants to deploy the analytics pipeline (behavior creation, incident detection): hand off to `vss-setup-behavior-analytics`.\n- If the user wants to publish a runtime config \u002F calibration update through the REST API: confirm Kafka is reachable, then use the `\u002Fconfig` or calibration endpoints and point them at the behavior-analytics dynamic-update references for the consumer wire contract.\n- If the user wants to understand the dynamic config \u002F dynamic calibration wire contract from the **consumer** (behavior-analytics) side: point them at the `vss-setup-behavior-analytics` dynamic-config and dynamic-calibration references.\n- If the user wants to query or interact with the REST API endpoints: the deploy reference endpoint table covers what's available. For the full OpenAPI spec, see `src\u002Fapp\u002Fspecification\u002Fopenapi.json` in the `video-analytics-api` repo.\n\n\nbump:1\n",{"name":5,"license":15,"metadata":63,"description":7},{"tags":64,"author":65,"version":66,"github-url":67},"nvidia blueprint operational deployment video-analytics-api rest-api","NVIDIA Video Search and Summarization team","3.2.0","https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Fvss-setup-video-analytics-api","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-setup-video-analytics-api",{"tool":81,"command":82},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-setup-video-analytics-api",{"tool":84,"command":85},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-setup-video-analytics-api","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002FSKILL.md",true,[89,95,99,103,107,111,115,119],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"BENCHMARK.md","REFERENCE","参考文档",3933,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002FBENCHMARK.md",{"path":96,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":97,"sourceHtmlUrl":98},"evals\u002Fevals.json",697,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Fevals\u002Fevals.json",{"path":100,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":101,"sourceHtmlUrl":102},"evals\u002Fstandalone_deploy.json",3932,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Fevals\u002Fstandalone_deploy.json",{"path":104,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fconfiguration.md",4078,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Freferences\u002Fconfiguration.md",{"path":108,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fdeploy-video-analytics-api-service.md",15349,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Freferences\u002Fdeploy-video-analytics-api-service.md",{"path":112,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":113,"sourceHtmlUrl":114},"references\u002Fngc-api-key-registry-login.md",2018,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Freferences\u002Fngc-api-key-registry-login.md",{"path":116,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":117,"sourceHtmlUrl":118},"skill-card.md",3964,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Fskill-card.md",{"path":120,"resourceType":121,"resourceTypeDesc":122,"sizeBytes":123,"sourceHtmlUrl":124},"skill.oms.sig","OTHER","其他",5613,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-setup-video-analytics-api\u002Fskill.oms.sig",[126,150,174,200,218,251],{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":139,"publishedAt":58},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. 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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",[133,136],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[140,141,142,143,144,51,145,146,147,148,149],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":158,"publishedAt":58},"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",[156,157],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},[159,160,142,161,162,163,164,165,51,166,167,168,169,170,145,146,171,172,57,173],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":184,"publishedAt":58},"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",[180,183],{"categoryNo":181,"categoryName":182,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},[185,186,187,188,189,190,191,192,51,193,194,195,196,197,198,57,199],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":208,"publishedAt":58},"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",[206,207],{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},[209,142,210,161,211,212,213,214,144,51,215,169,145,146,216,217],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":231,"publishedAt":58},"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",[224,227,228],{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":181,"categoryName":182,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":229,"categoryName":230,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000032","设计师",[232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,146,250],"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":252,"name":253,"nameZh":6,"description":254,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":130,"licenseFileUrl":6,"sourceHtmlUrl":255,"categories":256,"keywords":259,"publishedAt":58},"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",[257,258],{"categoryNo":181,"categoryName":182,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":229,"categoryName":230,"taxonomyType":32,"taxonomyTypeDesc":33},[232,234,260,241,244,51,261,262,170,249,263,146,171,264,57,265,266],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]