[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PT7QCPRFPCP9TPTZ47G4F7":3,"skill-same:01M6PT7QCPRFPCP9TPTZ47G4F7":126},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6PT7QCPRFPCP9TPTZ47G4F7","vss-deploy-video-embedding",null,"Use this skill when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice. Covers Docker Compose bring-up, GPU and storage prerequisites, the `\u002Fv1` REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis\u002FKafka\u002FOTel integration, common failure modes, and teardown.","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-deploy-video-embedding\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"deploy","vss deploy video embedding","embedding","Video","search","embeddings","kafka","microservice","api","metrics","text","common","up","Blueprint","teardown","live","operating","docker compose","streams","storage","2026-11-10 10:12:30",false,"---\nname: vss-deploy-video-embedding\ndescription: >\n  Use this skill when deploying, operating, or integrating the VSS 3.2 GA\n  RT-Embed Video Embedding microservice. Covers Docker Compose bring-up,\n  GPU and storage prerequisites, the `\u002Fv1` REST API (file uploads,\n  text and video embeddings, live RTSP streams, health and metrics),\n  Redis\u002FKafka\u002FOTel integration, common failure modes, and teardown.\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 deployment\"\n---\n\n# VSS Video Embedding (RT-Embed)\n\nUse this skill when you need to:\n\n- Deploy the VSS Video Embedding microservice from a Docker Compose file.\n- Generate text or video embeddings against the Cosmos-Embed1-448p model.\n- Embed an uploaded file, an HTTP\u002FS3\u002Ffile\u002Fdata URL, or a live RTSP stream.\n- Wire the service into a VSS deployment alongside Redis, Kafka, and OpenTelemetry.\n- Triage readiness, model-download, GPU, or stream-reconnection failures.\n\n**Trigger phrases:** `vss-deploy-video-embedding`, `RT-Embed`, `rtvi-embed`, `video embedding service`, `Cosmos-Embed1`, `embed live stream`, `embed video file`, `generate video embeddings`, `text embedding for video search`.\n\n## Service Snapshot\n\n- **VSS 3.2 GA skill:** `vss-deploy-video-embedding`.\n- **Legacy 3.1 name:** RT-Embed.\n- **Compose service:** `rtvi-embed`.\n- **Container name:** `vss-rtvi-embed`.\n- **Image:** `nvcr.io\u002Fnvidia\u002Fvss-core\u002Fvss-rt-embed` (override with `RTVI_EMBED_IMAGE`).\n- **Default tag:** `3.2.0` (override with `RTVI_EMBED_TAG`).\n- **Profile:** `bp_developer_search_2d`.\n- **Container port:** `8000` (host-side `${RTVI_EMBED_PORT}`).\n- **Default model:** `cosmos-embed1-448p` from `nvidia\u002FCosmos-Embed1-448p`.\n- **Health endpoint:** `GET \u002Fv1\u002Fready`.\n- **Healthcheck startup grace:** `1200s` (20 minutes) on first boot.\n\n## Prerequisites\n\nBefore bringing the service up:\n\n1. NVIDIA driver + NVIDIA Container Toolkit installed; default runtime set to `nvidia`.\n2. Docker Engine and Docker Compose plugin recent enough to support `${VAR:+value}` conditional volume substitution.\n3. `docker login nvcr.io` completed with `$oauthtoken` and a valid NGC API key.\n4. Host environment provides at minimum: `RTVI_EMBED_PORT`, `VSS_DATA_DIR`, `NGC_API_KEY`, and optionally `HF_TOKEN` to avoid Hugging Face 429 rate-limit errors during the Cosmos-Embed1 weights download.\n5. Free disk space for persistent caches: `rtvi-hf-cache`, `rtvi-ngc-model-cache`, `rtvi-triton-model-repo` (multi-GB).\n\nSee `references\u002Fdeploy-vss-deploy-video-embedding.md` for the full prerequisite list and `references\u002Fenvironment.md` for the variable matrix.\n\n## Deploy\n\nFor **standalone RT-Embed**, work from the service directory:\n\n```bash\ncd \"{{repo_root}}\u002Fdeploy\u002Fdocker\u002Fservices\u002Frtvi\u002Frtvi-embed\"\n```\n\nDo **not** use `\u002Fvss-deploy-profile` or `scripts\u002Fdev-profile.sh` for this standalone deployment.\n\nFor agent-driven validation, never let `sudo` prompt interactively. Before any\nprivileged ownership or Docker operation, use the non-interactive guard in\n[`references\u002Fdeploy-vss-deploy-video-embedding.md`](references\u002Fdeploy-vss-deploy-video-embedding.md)\nand [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md): prefer plain\n`docker`; otherwise use `sudo -n docker`; if `sudo -n` fails, stop with the exact\nmanual command for the host owner instead of retrying with interactive sudo or\nweakening permissions.\n\nSet a minimal standalone environment before `docker compose up`. If `sudo -n chown`\nfails, stop before `docker compose up` and ask the host owner to run the printed\ncommand.\n\n```bash\nexport RTVI_EMBED_PORT=8017\nexport VSS_DATA_DIR=\"${VSS_DATA_DIR:-$(pwd)\u002F.standalone-data}\"\nexport NGC_API_KEY=\"\u003Cyour-ngc-api-key>\"\nexport HOST_IP=\"$(hostname -I | awk '{print $1}')\"\nexport HF_TOKEN=\"${HF_TOKEN:-}\"  # optional, but recommended to avoid HF 429s\nexport RTVI_EMBED_KAFKA_ENABLED=false\nexport ENABLE_REDIS_ERROR_MESSAGES=false\n# Prepare VST clip-storage host dir; use `sudo -n` for ownership fixes.\nCLIP_STORAGE_DIR=\"${VSS_DATA_DIR}\u002Fdata_log\u002Fvst\u002Fclip_storage\"\nmkdir -p \"$CLIP_STORAGE_DIR\"\nif ! sudo -n chown -R 1001:1001 \"$CLIP_STORAGE_DIR\"; then\n  echo \"ERROR: passwordless sudo is unavailable for host-path ownership.\" >&2\n  echo \"Ask the host owner to run: sudo chown -R 1001:1001 \\\"$CLIP_STORAGE_DIR\\\"\" >&2\n  echo \"Do not work around this with chmod 777 or world-writable permissions.\" >&2\n  return 1 2>\u002Fdev\u002Fnull || exit 1\nfi\n```\n\nThis avoids mounting `\u002Fdata_log\u002Fvst\u002Fclip_storage` from filesystem root when `VSS_DATA_DIR` is unset, and prevents startup stalls from missing Kafka\u002FRedis peers in standalone mode.\n\n```bash\n# Bring up the service under the required Compose profile.\ndocker compose -f rtvi-embed-docker-compose.yml \\\n  --profile bp_developer_search_2d up -d rtvi-embed\n```\n\nIf Docker requires elevated privileges, use `sudo -n docker compose ...` and fail\nfast if `sudo -n` reports that a password is required.\n\n```bash\n# Watch logs while the model downloads and Triton repo builds.\ndocker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed\n```\n\nFirst-boot startup may take 20 minutes for the Cosmos-Embed1 download and Triton model repository build. Do not shorten the `start_period: 1200s` healthcheck during the first boot or the container will be marked unhealthy while still warming up.\n\n### Verify\n\n```bash\nBASE_URL=\"http:\u002F\u002Flocalhost:${RTVI_EMBED_PORT}\"\n\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fready\"               # 200 when warm.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fready?detailed=true\" # Component-level status.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fversion\"\nMODELS_JSON=$(curl -fsS \"$BASE_URL\u002Fv1\u002Fmodels\")\necho \"$MODELS_JSON\"                          # Confirms cosmos-embed1-448p is loaded.\n\nMODEL_ID=\"$(echo \"$MODELS_JSON\" | jq -r '.data[0].id \u002F\u002F empty')\"\ntest -n \"$MODEL_ID\" || { echo \"ERROR: \u002Fv1\u002Fmodels has no model id — wait until \u002Fv1\u002Fready is 200\" >&2; exit 1; }\n```\n\nThe sections below that call the API reuse `$BASE_URL` and `$MODEL_ID` from this block.\n\n## Common Operations\n\n### Generate video embeddings from an uploaded file\n\n```bash\nFILE_ID=$(curl -fsS -X POST \"$BASE_URL\u002Fv1\u002Ffiles\" \\\n  -F purpose=vision \\\n  -F media_type=video \\\n  -F file=@\u002Fpath\u002Fto\u002Fclip.mp4 | jq -r .id)\n\ncurl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d \"{\n    \\\"id\\\": \\\"$FILE_ID\\\",\n    \\\"model\\\": \\\"$MODEL_ID\\\",\n    \\\"chunk_duration\\\": 60,\n    \\\"chunk_overlap_duration\\\": 10\n  }\"\n```\n\n### Generate text embeddings (for text-to-video search)\n\n```bash\ncurl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_text_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d \"{\\\"text_input\\\":\\\"a forklift moving pallets\\\",\\\"model\\\":\\\"${MODEL_ID}\\\"}\"\n```\n\n### Embed a live RTSP stream\n\nLive streams **require** `stream: true` and `chunk_duration > 0`. A synchronous call returns `400 BadParameters: \"Only streaming output is supported for live-streams\"`, and the `chunk_duration: 0` returned by `streams\u002Fadd` is a placeholder — it must be overridden on the embed request or you get `400 BadParameter: \"chunk_duration must be greater than 0\"`.\n\n`POST \u002Fv1\u002Fstreams\u002Fadd` does **not** deduplicate by `liveStreamUrl` — submitting the same URL twice mints two distinct `stream_id`s. Before adding, call `GET \u002Fv1\u002Fstreams\u002Fget-stream-info` and reuse any existing registration for that URL to avoid orphaned entries.\n\n```bash\nSTREAM_ID=$(curl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fstreams\u002Fadd\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"streams\":[{\"liveStreamUrl\":\"rtsp:\u002F\u002Fhost:port\u002Flive\u002Fvideo\",\"description\":\"camera-001\"}]}' \\\n  | jq -r '.results[0].id')\n\ncurl -N -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -H \"Accept: text\u002Fevent-stream\" \\\n  -d \"{\n    \\\"id\\\": \\\"$STREAM_ID\\\",\n    \\\"model\\\": \\\"$MODEL_ID\\\",\n    \\\"stream\\\": true,\n    \\\"chunk_duration\\\": 10,\n    \\\"chunk_overlap_duration\\\": 2\n  }\"\n\n# List registered live streams (use this to recover stream_ids across sessions).\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fstreams\u002Fget-stream-info\"\n\n# Stop embedding for the stream when done (terminates SSE with data: [DONE]).\ncurl -fsS -X DELETE \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\u002F$STREAM_ID\"\n```\n\nSee `references\u002Frest-api.md` for the full endpoint catalog, SSE streaming, and single-stream control-plane patterns.\n\n## Logs, Metrics, And Status\n\n```bash\ndocker compose -f rtvi-embed-docker-compose.yml ps\ndocker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed\ndocker stats vss-rtvi-embed\n\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fmetrics\"          # Prometheus.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fassets\u002Fstats\"     # Asset storage counts and TTL.\n```\n\nIf `RTVI_EMBED_LOG_DIR` is bound to a host directory, log files are also available at `\u002Fopt\u002Fnvidia\u002Frtvi\u002Flog\u002Frtvi\u002F` on the host.\n\n## Integration Surface\n\n- **Inputs:** REST API on `:${RTVI_EMBED_PORT}` (`POST \u002Fv1\u002Ffiles`, `POST \u002Fv1\u002Fgenerate_text_embeddings`, `POST \u002Fv1\u002Fgenerate_video_embeddings`, live-stream control endpoints).\n- **Outputs:** Synchronous REST responses, optional SSE for chunked video embeddings, optional Kafka messages on the topics named by `RTVI_EMBED_KAFKA_TOPIC` (container `KAFKA_TOPIC`) and `RTVI_EMBED_ERROR_MESSAGE_TOPIC` (container `ERROR_MESSAGE_TOPIC`) when Kafka is enabled (host: `RTVI_EMBED_KAFKA_ENABLED=true`, which Compose maps to container `KAFKA_ENABLED`).\n- **Optional peers:** Redis (`ENABLE_REDIS_ERROR_MESSAGES=true`), Kafka (host: `RTVI_EMBED_KAFKA_ENABLED=true` → container `KAFKA_ENABLED`), OpenTelemetry collector (host: `RTVI_EMBED_ENABLE_OTEL_MONITORING=true` → container `ENABLE_OTEL_MONITORING`).\n\n`references\u002Fintegrate-vss-deploy-video-embedding.md` documents the full integration contract.\n\n## Error Handling\n\nAPI failures return JSON with `code` and `message` fields:\n\n```json\n{\n  \"code\": \"BadParameter\",\n  \"message\": \"chunk_duration must be greater than 0\"\n}\n```\n\nPydantic \u002F OpenAPI validation failures use HTTP `422` with `code: \"InvalidParameters\"` and a field-level `message`.\n\n| Code | Meaning | Common Cause |\n|------|---------|--------------|\n| 400 | Bad Request | Missing `text_input`; unknown `file_id` \u002F `stream_id` \u002F `model`; live stream called without `stream: true`; `chunk_duration: 0` on a live-stream embed request; `chunk_overlap_duration >= chunk_duration` |\n| 401 | Unauthorized | Missing or invalid `Authorization: Bearer \u003Ctoken>` when the deployment enforces auth |\n| 403 | Forbidden | `file:\u002F\u002F` URLs disabled (`FILE_URL_ALLOWED_DIRS` unset) or resolved path outside the allow-list (`code: \"Forbidden\"`) |\n| 409 | Conflict | `DELETE \u002Fv1\u002Ffiles\u002F{file_id}` while the file is in use (`ResourceInUse`); another client already connected to the same live stream (`Conflict`) |\n| 413 | Payload Too Large | Uploaded file or decoded `data:` URI exceeds server size limits |\n| 422 | Unprocessable Entity | Schema validation failure — malformed UUID, wrong multipart field types, invalid enum values; invalid URL format for supported schemes |\n| 429 | Rate Limited | Request rate exceeded — retry with exponential backoff |\n| 500 | Internal Server Error | Unexpected inference or I\u002FO failure — inspect `docker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed` |\n| 503 | Service Unavailable | `\u002Fv1\u002Fready` still warming up (model download \u002F Triton repo build); embedding endpoint busy with another file or text query; max live streams reached; CUDA OOM during inference |\n\n**503 on `\u002Fv1\u002Fready` during first boot is expected** until Cosmos-Embed1 finishes downloading and the Triton model repo is built (up to ~20 minutes). Do not treat it as an application error until after the healthcheck `start_period: 1200s` elapses.\n\n**503 on embedding endpoints** with message `\"Server is busy processing another file or text\"` or `\"Server is busy processing another file \u002F live-stream.\"` means the service handles one synchronous embed job at a time — retry with backoff or shard work across instances.\n\nFor endpoint-specific constraints (live-stream SSE requirements, URL schemes, response schemas), see [`references\u002Frest-api.md`](references\u002Frest-api.md). For Compose startup, cache, and permission failures, see [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md).\n\n## Troubleshooting\n\nFor common failure patterns and resolutions, see `references\u002Ftroubleshooting.md`. Frequent issues:\n\n- `\u002Fv1\u002Fready` stuck at 503 → check for missing `NGC_API_KEY`, Hugging Face 429 rate-limit failures during the first-boot model download (set `HF_TOKEN` to avoid), or unreachable Redis\u002FKafka peers when those flags are enabled.\n- Healthcheck flipping unhealthy in the first 20 minutes → restore `start_period: 1200s`.\n- Permission errors on bind-mounted cache directories → `sudo -n chown -R 1001:1001` on the host paths; if passwordless sudo is unavailable, ask the host owner to run the printed command (do not use `chmod 777`).\n- `sudo` prompts for a password during deploy → use `sudo -n` and fail fast; see `references\u002Ftroubleshooting.md`; never retry with interactive sudo in an agent session.\n\n## Upgrade And Rollback\n\nPin `RTVI_EMBED_IMAGE` \u002F `RTVI_EMBED_TAG`, pull, recreate with `--profile bp_developer_search_2d`, and wait for `\u002Fv1\u002Fready` before cutover. Named volumes persist across image swaps.\n\nFull steps: [Upgrade & Rollback](references\u002Fdeploy-vss-deploy-video-embedding.md#upgrade--rollback).\n\n## Tear Down\n\nStop the standalone stack with `docker compose -f rtvi-embed-docker-compose.yml down`. Use `down -v` only when you intend to destroy named model caches.\n\nFull steps and cache warnings: [Tear Down](references\u002Fdeploy-vss-deploy-video-embedding.md#tear-down).\n\n## References\n\n| File | When to read |\n|---|---|\n| [references\u002FREADME.md](references\u002FREADME.md) | Table of contents for all reference files. |\n| [references\u002Fdeploy-vss-deploy-video-embedding.md](references\u002Fdeploy-vss-deploy-video-embedding.md) | Build Vision Agent deployment reference: image, GPU, storage, startup, prerequisites, known issues. |\n| [references\u002Fintegrate-vss-deploy-video-embedding.md](references\u002Fintegrate-vss-deploy-video-embedding.md) | Build Vision Agent integration reference: peers, inputs\u002Foutputs, env vars, network, example Compose snippet. |\n| [references\u002Frest-api.md](references\u002Frest-api.md) | Full REST endpoint catalog with worked `curl` examples for file uploads, video\u002Ftext embeddings, live streams, and health\u002Fmetrics. |\n| [references\u002Fenvironment.md](references\u002Fenvironment.md) | Complete environment-variable matrix, including host-to-container renames and secret-sensitive variables. |\n| [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) | Operational diagnostics for startup, model\u002Fcache, runtime, and observability issues. |\n\n","# VSS Video Embedding (RT-Embed)\n\nUse this skill when you need to:\n\n- Deploy the VSS Video Embedding microservice from a Docker Compose file.\n- Generate text or video embeddings against the Cosmos-Embed1-448p model.\n- Embed an uploaded file, an HTTP\u002FS3\u002Ffile\u002Fdata URL, or a live RTSP stream.\n- Wire the service into a VSS deployment alongside Redis, Kafka, and OpenTelemetry.\n- Triage readiness, model-download, GPU, or stream-reconnection failures.\n\n**Trigger phrases:** `vss-deploy-video-embedding`, `RT-Embed`, `rtvi-embed`, `video embedding service`, `Cosmos-Embed1`, `embed live stream`, `embed video file`, `generate video embeddings`, `text embedding for video search`.\n\n## Service Snapshot\n\n- **VSS 3.2 GA skill:** `vss-deploy-video-embedding`.\n- **Legacy 3.1 name:** RT-Embed.\n- **Compose service:** `rtvi-embed`.\n- **Container name:** `vss-rtvi-embed`.\n- **Image:** `nvcr.io\u002Fnvidia\u002Fvss-core\u002Fvss-rt-embed` (override with `RTVI_EMBED_IMAGE`).\n- **Default tag:** `3.2.0` (override with `RTVI_EMBED_TAG`).\n- **Profile:** `bp_developer_search_2d`.\n- **Container port:** `8000` (host-side `${RTVI_EMBED_PORT}`).\n- **Default model:** `cosmos-embed1-448p` from `nvidia\u002FCosmos-Embed1-448p`.\n- **Health endpoint:** `GET \u002Fv1\u002Fready`.\n- **Healthcheck startup grace:** `1200s` (20 minutes) on first boot.\n\n## Prerequisites\n\nBefore bringing the service up:\n\n1. NVIDIA driver + NVIDIA Container Toolkit installed; default runtime set to `nvidia`.\n2. Docker Engine and Docker Compose plugin recent enough to support `${VAR:+value}` conditional volume substitution.\n3. `docker login nvcr.io` completed with `$oauthtoken` and a valid NGC API key.\n4. Host environment provides at minimum: `RTVI_EMBED_PORT`, `VSS_DATA_DIR`, `NGC_API_KEY`, and optionally `HF_TOKEN` to avoid Hugging Face 429 rate-limit errors during the Cosmos-Embed1 weights download.\n5. Free disk space for persistent caches: `rtvi-hf-cache`, `rtvi-ngc-model-cache`, `rtvi-triton-model-repo` (multi-GB).\n\nSee `references\u002Fdeploy-vss-deploy-video-embedding.md` for the full prerequisite list and `references\u002Fenvironment.md` for the variable matrix.\n\n## Deploy\n\nFor **standalone RT-Embed**, work from the service directory:\n\n```bash\ncd \"{{repo_root}}\u002Fdeploy\u002Fdocker\u002Fservices\u002Frtvi\u002Frtvi-embed\"\n```\n\nDo **not** use `\u002Fvss-deploy-profile` or `scripts\u002Fdev-profile.sh` for this standalone deployment.\n\nFor agent-driven validation, never let `sudo` prompt interactively. Before any\nprivileged ownership or Docker operation, use the non-interactive guard in\n[`references\u002Fdeploy-vss-deploy-video-embedding.md`](references\u002Fdeploy-vss-deploy-video-embedding.md)\nand [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md): prefer plain\n`docker`; otherwise use `sudo -n docker`; if `sudo -n` fails, stop with the exact\nmanual command for the host owner instead of retrying with interactive sudo or\nweakening permissions.\n\nSet a minimal standalone environment before `docker compose up`. If `sudo -n chown`\nfails, stop before `docker compose up` and ask the host owner to run the printed\ncommand.\n\n```bash\nexport RTVI_EMBED_PORT=8017\nexport VSS_DATA_DIR=\"${VSS_DATA_DIR:-$(pwd)\u002F.standalone-data}\"\nexport NGC_API_KEY=\"\u003Cyour-ngc-api-key>\"\nexport HOST_IP=\"$(hostname -I | awk '{print $1}')\"\nexport HF_TOKEN=\"${HF_TOKEN:-}\"  # optional, but recommended to avoid HF 429s\nexport RTVI_EMBED_KAFKA_ENABLED=false\nexport ENABLE_REDIS_ERROR_MESSAGES=false\n# Prepare VST clip-storage host dir; use `sudo -n` for ownership fixes.\nCLIP_STORAGE_DIR=\"${VSS_DATA_DIR}\u002Fdata_log\u002Fvst\u002Fclip_storage\"\nmkdir -p \"$CLIP_STORAGE_DIR\"\nif ! sudo -n chown -R 1001:1001 \"$CLIP_STORAGE_DIR\"; then\n  echo \"ERROR: passwordless sudo is unavailable for host-path ownership.\" >&2\n  echo \"Ask the host owner to run: sudo chown -R 1001:1001 \\\"$CLIP_STORAGE_DIR\\\"\" >&2\n  echo \"Do not work around this with chmod 777 or world-writable permissions.\" >&2\n  return 1 2>\u002Fdev\u002Fnull || exit 1\nfi\n```\n\nThis avoids mounting `\u002Fdata_log\u002Fvst\u002Fclip_storage` from filesystem root when `VSS_DATA_DIR` is unset, and prevents startup stalls from missing Kafka\u002FRedis peers in standalone mode.\n\n```bash\n# Bring up the service under the required Compose profile.\ndocker compose -f rtvi-embed-docker-compose.yml \\\n  --profile bp_developer_search_2d up -d rtvi-embed\n```\n\nIf Docker requires elevated privileges, use `sudo -n docker compose ...` and fail\nfast if `sudo -n` reports that a password is required.\n\n```bash\n# Watch logs while the model downloads and Triton repo builds.\ndocker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed\n```\n\nFirst-boot startup may take 20 minutes for the Cosmos-Embed1 download and Triton model repository build. Do not shorten the `start_period: 1200s` healthcheck during the first boot or the container will be marked unhealthy while still warming up.\n\n### Verify\n\n```bash\nBASE_URL=\"http:\u002F\u002Flocalhost:${RTVI_EMBED_PORT}\"\n\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fready\"               # 200 when warm.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fready?detailed=true\" # Component-level status.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fversion\"\nMODELS_JSON=$(curl -fsS \"$BASE_URL\u002Fv1\u002Fmodels\")\necho \"$MODELS_JSON\"                          # Confirms cosmos-embed1-448p is loaded.\n\nMODEL_ID=\"$(echo \"$MODELS_JSON\" | jq -r '.data[0].id \u002F\u002F empty')\"\ntest -n \"$MODEL_ID\" || { echo \"ERROR: \u002Fv1\u002Fmodels has no model id — wait until \u002Fv1\u002Fready is 200\" >&2; exit 1; }\n```\n\nThe sections below that call the API reuse `$BASE_URL` and `$MODEL_ID` from this block.\n\n## Common Operations\n\n### Generate video embeddings from an uploaded file\n\n```bash\nFILE_ID=$(curl -fsS -X POST \"$BASE_URL\u002Fv1\u002Ffiles\" \\\n  -F purpose=vision \\\n  -F media_type=video \\\n  -F file=@\u002Fpath\u002Fto\u002Fclip.mp4 | jq -r .id)\n\ncurl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d \"{\n    \\\"id\\\": \\\"$FILE_ID\\\",\n    \\\"model\\\": \\\"$MODEL_ID\\\",\n    \\\"chunk_duration\\\": 60,\n    \\\"chunk_overlap_duration\\\": 10\n  }\"\n```\n\n### Generate text embeddings (for text-to-video search)\n\n```bash\ncurl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_text_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d \"{\\\"text_input\\\":\\\"a forklift moving pallets\\\",\\\"model\\\":\\\"${MODEL_ID}\\\"}\"\n```\n\n### Embed a live RTSP stream\n\nLive streams **require** `stream: true` and `chunk_duration > 0`. A synchronous call returns `400 BadParameters: \"Only streaming output is supported for live-streams\"`, and the `chunk_duration: 0` returned by `streams\u002Fadd` is a placeholder — it must be overridden on the embed request or you get `400 BadParameter: \"chunk_duration must be greater than 0\"`.\n\n`POST \u002Fv1\u002Fstreams\u002Fadd` does **not** deduplicate by `liveStreamUrl` — submitting the same URL twice mints two distinct `stream_id`s. Before adding, call `GET \u002Fv1\u002Fstreams\u002Fget-stream-info` and reuse any existing registration for that URL to avoid orphaned entries.\n\n```bash\nSTREAM_ID=$(curl -fsS -X POST \"$BASE_URL\u002Fv1\u002Fstreams\u002Fadd\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"streams\":[{\"liveStreamUrl\":\"rtsp:\u002F\u002Fhost:port\u002Flive\u002Fvideo\",\"description\":\"camera-001\"}]}' \\\n  | jq -r '.results[0].id')\n\ncurl -N -X POST \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -H \"Accept: text\u002Fevent-stream\" \\\n  -d \"{\n    \\\"id\\\": \\\"$STREAM_ID\\\",\n    \\\"model\\\": \\\"$MODEL_ID\\\",\n    \\\"stream\\\": true,\n    \\\"chunk_duration\\\": 10,\n    \\\"chunk_overlap_duration\\\": 2\n  }\"\n\n# List registered live streams (use this to recover stream_ids across sessions).\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fstreams\u002Fget-stream-info\"\n\n# Stop embedding for the stream when done (terminates SSE with data: [DONE]).\ncurl -fsS -X DELETE \"$BASE_URL\u002Fv1\u002Fgenerate_video_embeddings\u002F$STREAM_ID\"\n```\n\nSee `references\u002Frest-api.md` for the full endpoint catalog, SSE streaming, and single-stream control-plane patterns.\n\n## Logs, Metrics, And Status\n\n```bash\ndocker compose -f rtvi-embed-docker-compose.yml ps\ndocker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed\ndocker stats vss-rtvi-embed\n\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fmetrics\"          # Prometheus.\ncurl -fsS \"$BASE_URL\u002Fv1\u002Fassets\u002Fstats\"     # Asset storage counts and TTL.\n```\n\nIf `RTVI_EMBED_LOG_DIR` is bound to a host directory, log files are also available at `\u002Fopt\u002Fnvidia\u002Frtvi\u002Flog\u002Frtvi\u002F` on the host.\n\n## Integration Surface\n\n- **Inputs:** REST API on `:${RTVI_EMBED_PORT}` (`POST \u002Fv1\u002Ffiles`, `POST \u002Fv1\u002Fgenerate_text_embeddings`, `POST \u002Fv1\u002Fgenerate_video_embeddings`, live-stream control endpoints).\n- **Outputs:** Synchronous REST responses, optional SSE for chunked video embeddings, optional Kafka messages on the topics named by `RTVI_EMBED_KAFKA_TOPIC` (container `KAFKA_TOPIC`) and `RTVI_EMBED_ERROR_MESSAGE_TOPIC` (container `ERROR_MESSAGE_TOPIC`) when Kafka is enabled (host: `RTVI_EMBED_KAFKA_ENABLED=true`, which Compose maps to container `KAFKA_ENABLED`).\n- **Optional peers:** Redis (`ENABLE_REDIS_ERROR_MESSAGES=true`), Kafka (host: `RTVI_EMBED_KAFKA_ENABLED=true` → container `KAFKA_ENABLED`), OpenTelemetry collector (host: `RTVI_EMBED_ENABLE_OTEL_MONITORING=true` → container `ENABLE_OTEL_MONITORING`).\n\n`references\u002Fintegrate-vss-deploy-video-embedding.md` documents the full integration contract.\n\n## Error Handling\n\nAPI failures return JSON with `code` and `message` fields:\n\n```json\n{\n  \"code\": \"BadParameter\",\n  \"message\": \"chunk_duration must be greater than 0\"\n}\n```\n\nPydantic \u002F OpenAPI validation failures use HTTP `422` with `code: \"InvalidParameters\"` and a field-level `message`.\n\n| Code | Meaning | Common Cause |\n|------|---------|--------------|\n| 400 | Bad Request | Missing `text_input`; unknown `file_id` \u002F `stream_id` \u002F `model`; live stream called without `stream: true`; `chunk_duration: 0` on a live-stream embed request; `chunk_overlap_duration >= chunk_duration` |\n| 401 | Unauthorized | Missing or invalid `Authorization: Bearer \u003Ctoken>` when the deployment enforces auth |\n| 403 | Forbidden | `file:\u002F\u002F` URLs disabled (`FILE_URL_ALLOWED_DIRS` unset) or resolved path outside the allow-list (`code: \"Forbidden\"`) |\n| 409 | Conflict | `DELETE \u002Fv1\u002Ffiles\u002F{file_id}` while the file is in use (`ResourceInUse`); another client already connected to the same live stream (`Conflict`) |\n| 413 | Payload Too Large | Uploaded file or decoded `data:` URI exceeds server size limits |\n| 422 | Unprocessable Entity | Schema validation failure — malformed UUID, wrong multipart field types, invalid enum values; invalid URL format for supported schemes |\n| 429 | Rate Limited | Request rate exceeded — retry with exponential backoff |\n| 500 | Internal Server Error | Unexpected inference or I\u002FO failure — inspect `docker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed` |\n| 503 | Service Unavailable | `\u002Fv1\u002Fready` still warming up (model download \u002F Triton repo build); embedding endpoint busy with another file or text query; max live streams reached; CUDA OOM during inference |\n\n**503 on `\u002Fv1\u002Fready` during first boot is expected** until Cosmos-Embed1 finishes downloading and the Triton model repo is built (up to ~20 minutes). Do not treat it as an application error until after the healthcheck `start_period: 1200s` elapses.\n\n**503 on embedding endpoints** with message `\"Server is busy processing another file or text\"` or `\"Server is busy processing another file \u002F live-stream.\"` means the service handles one synchronous embed job at a time — retry with backoff or shard work across instances.\n\nFor endpoint-specific constraints (live-stream SSE requirements, URL schemes, response schemas), see [`references\u002Frest-api.md`](references\u002Frest-api.md). For Compose startup, cache, and permission failures, see [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md).\n\n## Troubleshooting\n\nFor common failure patterns and resolutions, see `references\u002Ftroubleshooting.md`. Frequent issues:\n\n- `\u002Fv1\u002Fready` stuck at 503 → check for missing `NGC_API_KEY`, Hugging Face 429 rate-limit failures during the first-boot model download (set `HF_TOKEN` to avoid), or unreachable Redis\u002FKafka peers when those flags are enabled.\n- Healthcheck flipping unhealthy in the first 20 minutes → restore `start_period: 1200s`.\n- Permission errors on bind-mounted cache directories → `sudo -n chown -R 1001:1001` on the host paths; if passwordless sudo is unavailable, ask the host owner to run the printed command (do not use `chmod 777`).\n- `sudo` prompts for a password during deploy → use `sudo -n` and fail fast; see `references\u002Ftroubleshooting.md`; never retry with interactive sudo in an agent session.\n\n## Upgrade And Rollback\n\nPin `RTVI_EMBED_IMAGE` \u002F `RTVI_EMBED_TAG`, pull, recreate with `--profile bp_developer_search_2d`, and wait for `\u002Fv1\u002Fready` before cutover. Named volumes persist across image swaps.\n\nFull steps: [Upgrade & Rollback](references\u002Fdeploy-vss-deploy-video-embedding.md#upgrade--rollback).\n\n## Tear Down\n\nStop the standalone stack with `docker compose -f rtvi-embed-docker-compose.yml down`. Use `down -v` only when you intend to destroy named model caches.\n\nFull steps and cache warnings: [Tear Down](references\u002Fdeploy-vss-deploy-video-embedding.md#tear-down).\n\n## References\n\n| File | When to read |\n|---|---|\n| [references\u002FREADME.md](references\u002FREADME.md) | Table of contents for all reference files. |\n| [references\u002Fdeploy-vss-deploy-video-embedding.md](references\u002Fdeploy-vss-deploy-video-embedding.md) | Build Vision Agent deployment reference: image, GPU, storage, startup, prerequisites, known issues. |\n| [references\u002Fintegrate-vss-deploy-video-embedding.md](references\u002Fintegrate-vss-deploy-video-embedding.md) | Build Vision Agent integration reference: peers, inputs\u002Foutputs, env vars, network, example Compose snippet. |\n| [references\u002Frest-api.md](references\u002Frest-api.md) | Full REST endpoint catalog with worked `curl` examples for file uploads, video\u002Ftext embeddings, live streams, and health\u002Fmetrics. |\n| [references\u002Fenvironment.md](references\u002Fenvironment.md) | Complete environment-variable matrix, including host-to-container renames and secret-sensitive variables. |\n| [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) | Operational diagnostics for startup, model\u002Fcache, runtime, and observability issues. |\n\n",{"name":5,"license":15,"metadata":52,"description":56},{"tags":53,"version":54,"github-url":55},"nvidia blueprint operational deployment","3.2.0","https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization","Use this skill when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice. Covers Docker Compose bring-up, GPU and storage prerequisites, the `\u002Fv1` REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis\u002FKafka\u002FOTel integration, common failure modes, and teardown.\n","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Fvss-deploy-video-embedding","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":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-deploy-video-embedding",{"tool":70,"command":71},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-deploy-video-embedding",{"tool":73,"command":74},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill vss-deploy-video-embedding","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002FSKILL.md",true,[78,84,88,92,96,100,104,108,112,116,120],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"BENCHMARK.md","REFERENCE","参考文档",4282,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002FBENCHMARK.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"evals\u002Fevals.json",1517,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Fevals\u002Fevals.json",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"evals\u002Fstandalone_deploy.json",4146,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Fevals\u002Fstandalone_deploy.json",{"path":93,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fdeploy-vss-deploy-video-embedding.md",11360,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002Fdeploy-vss-deploy-video-embedding.md",{"path":97,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fenvironment.md",6398,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002Fenvironment.md",{"path":101,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fintegrate-vss-deploy-video-embedding.md",16785,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002Fintegrate-vss-deploy-video-embedding.md",{"path":105,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002FREADME.md",1535,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002FREADME.md",{"path":109,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Frest-api.md",12895,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002Frest-api.md",{"path":113,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":114,"sourceHtmlUrl":115},"references\u002Ftroubleshooting.md",8792,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Freferences\u002Ftroubleshooting.md",{"path":117,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":118,"sourceHtmlUrl":119},"skill-card.md",4105,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Fskill-card.md",{"path":121,"resourceType":122,"resourceTypeDesc":123,"sizeBytes":124,"sourceHtmlUrl":125},"skill.oms.sig","OTHER","其他",6325,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvss-deploy-video-embedding\u002Fskill.oms.sig",[127,152,177,203,221,256],{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":140,"publishedAt":47},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". 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",[134,137],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[141,142,143,144,145,146,147,148,149,150,151],"xunit","xunit skill","testing","injection","user","use","tests","triggers","unit","unit-testing","C#",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":160,"publishedAt":47},"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",[158,159],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},[161,162,143,163,164,165,166,167,146,168,169,170,171,172,147,148,173,174,175,176],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","for","objective",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":187,"publishedAt":47},"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",[183,186],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},[188,189,190,191,192,193,194,195,146,196,197,198,199,200,201,175,202],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":211,"publishedAt":47},"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",[209,210],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},[212,143,213,163,214,215,216,217,145,146,218,171,147,148,219,220],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":236,"publishedAt":47},"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",[227,230,231],{"categoryNo":228,"categoryName":229,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":232,"categoryName":233,"taxonomyType":234,"taxonomyTypeDesc":235},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,148,255],"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":257,"name":258,"nameZh":6,"description":259,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":131,"licenseFileUrl":6,"sourceHtmlUrl":260,"categories":261,"keywords":264,"publishedAt":47},"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",[262,263],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":232,"categoryName":233,"taxonomyType":234,"taxonomyTypeDesc":235},[237,239,265,246,249,146,266,267,172,254,268,148,173,269,175,270,271],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]