Vss Deploy Video Embedding
NVIDIA-AI-Blueprints/video-search-and-summarization
A skill your agent uses when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice.
A skill your agent uses when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice.
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills vss-deploy-video-embedding --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/vss-deploy-video-embedding .claude/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .claude/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embeddingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills vss-deploy-video-embedding --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/vss-deploy-video-embedding .agents/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .agents/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills vss-deploy-video-embedding --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/vss-deploy-video-embedding .cursor/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .cursor/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/vss-deploy-video-embedding--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills vss-deploy-video-embedding --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/vss-deploy-video-embedding .gemini/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .gemini/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills vss-deploy-video-embeddingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/vss-deploy-video-embedding .github/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .github/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills vss-deploy-video-embedding --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/vss-deploy-video-embedding .opencode/skills/vss-deploy-video-embedding && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "vss-deploy-video-embedding" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/vss-deploy-video-embedding into .opencode/skills/vss-deploy-video-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-deploy-video-embedding", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
vss-deploy-video-embeddingA skill your agent uses when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice.
Vss Deploy Video Embedding is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. 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 /v1 REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis/Kafka/OTel integration, common failure modes, and teardown.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `evals/standalone_deploy.json`).
It sits in AI & LLM Engineering, covering Embeddings. It works with Docker, NVIDIA AI Platform, Redis and OpenTelemetry. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
curldockerjqFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use curl and docker, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
HF_TOKENNGC_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Vss Deploy Video Embedding loads about 3.7k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 1,182 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
`docker`; otherwise use `sudo -n docker`; if `sudo -n` fails, stop with the exactner instead of retrying with interactive sudo orironment before `docker compose up`. If `sudo -n chown`Prepare VST clip-storage host dir; use `sudo -n` for ownership fixes.if ! sudo -n chown -R 1001:1001 "$CLIP_STORAGE_DIR"; thenecho "ERROR: passwordless sudo is unavailable for host-path ownership." >&2echo "Ask the host owner to run: sudo chown -R 1001:1001 \"$CLIP_STORAGE_DIR\"" >&2ocker requires elevated privileges, use `sudo -n docker compose ...` and failfast if `sudo -n` reports that a password is required.ors on bind-mounted cache directories → `sudo -n chown -R 1001:1001` on the host paths; if passwordless sudo is unavailaAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 1,182 words, ~3,661 tokens.
.claude/skills/vss-deploy-video-embedding/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.Use this skill when you need to:
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.
vss-deploy-video-embedding.rtvi-embed.vss-rtvi-embed.nvcr.io/nvidia/vss-core/vss-rt-embed (override with RTVI_EMBED_IMAGE).3.2.1 (override with RTVI_EMBED_TAG).bp_developer_search_2d.8000 (host-side ${RTVI_EMBED_PORT}).cosmos-embed1-448p from nvidia/Cosmos-Embed1-448p.GET /v1/ready.1200s (20 minutes) on first boot.Before bringing the service up:
nvidia.${VAR:+value} conditional volume substitution.docker login nvcr.io completed with $oauthtoken and a valid NGC API key.RTVI_EMBED_PORT, VSS_DATA_DIR, NGC_API_KEY, and optionally HF_TOKEN to avoid Hugging Face 429 rate-limit errors during the Cosmos-Embed1 weights download.rtvi-hf-cache, rtvi-ngc-model-cache, rtvi-triton-model-repo (multi-GB).See references/deploy-vss-deploy-video-embedding.md for the full prerequisite list and references/environment.md for the variable matrix.
For standalone RT-Embed, work from the service directory:
cd "{{repo_root}}/deploy/docker/services/rtvi/rtvi-embed"Do not use /vss-deploy-profile or scripts/dev-profile.sh for this standalone deployment.
For agent-driven validation, never let sudo prompt interactively. Before any
privileged ownership or Docker operation, use the non-interactive guard in
references/deploy-vss-deploy-video-embedding.md
and references/troubleshooting.md: prefer plain
docker; otherwise use sudo -n docker; if sudo -n fails, stop with the exact
manual command for the host owner instead of retrying with interactive sudo or
weakening permissions.
Set a minimal standalone environment before docker compose up. If sudo -n chown
fails, stop before docker compose up and ask the host owner to run the printed
command.
export RTVI_EMBED_PORT=8017
export VSS_DATA_DIR="${VSS_DATA_DIR:-$(pwd)/.standalone-data}"
export NGC_API_KEY="<your-ngc-api-key>"
export HOST_IP="$(hostname -I | awk '{print $1}')"
export HF_TOKEN="${HF_TOKEN:-}" # optional, but recommended to avoid HF 429s
export RTVI_EMBED_KAFKA_ENABLED=false
export ENABLE_REDIS_ERROR_MESSAGES=false
# Prepare VST clip-storage host dir; use `sudo -n` for ownership fixes.
CLIP_STORAGE_DIR="${VSS_DATA_DIR}/data_log/vst/clip_storage"
mkdir -p "$CLIP_STORAGE_DIR"
if ! sudo -n chown -R 1001:1001 "$CLIP_STORAGE_DIR"; then
echo "ERROR: passwordless sudo is unavailable for host-path ownership." >&2
echo "Ask the host owner to run: sudo chown -R 1001:1001 \"$CLIP_STORAGE_DIR\"" >&2
echo "Do not work around this with chmod 777 or world-writable permissions." >&2
return 1 2>/dev/null || exit 1
fiThis avoids mounting /data_log/vst/clip_storage from filesystem root when VSS_DATA_DIR is unset, and prevents startup stalls from missing Kafka/Redis peers in standalone mode.
# Bring up the service under the required Compose profile.
docker compose -f rtvi-embed-docker-compose.yml \
--profile bp_developer_search_2d up -d rtvi-embedIf Docker requires elevated privileges, use sudo -n docker compose ... and fail
fast if sudo -n reports that a password is required.
# Watch logs while the model downloads and Triton repo builds.
docker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embedFirst-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.
BASE_URL="http://localhost:${RTVI_EMBED_PORT}"
curl -fsS "$BASE_URL/v1/ready" # 200 when warm.
curl -fsS "$BASE_URL/v1/ready?detailed=true" # Component-level status.
curl -fsS "$BASE_URL/v1/version"
MODELS_JSON=$(curl -fsS "$BASE_URL/v1/models")
echo "$MODELS_JSON" # Confirms cosmos-embed1-448p is loaded.
MODEL_ID="$(echo "$MODELS_JSON" | jq -r '.data[0].id // empty')"
test -n "$MODEL_ID" || { echo "ERROR: /v1/models has no model id — wait until /v1/ready is 200" >&2; exit 1; }The sections below that call the API reuse $BASE_URL and $MODEL_ID from this block.
FILE_ID=$(curl -fsS -X POST "$BASE_URL/v1/files" \
-F purpose=vision \
-F media_type=video \
-F file=@/path/to/clip.mp4 | jq -r .id)
curl -fsS -X POST "$BASE_URL/v1/generate_video_embeddings" \
-H "Content-Type: application/json" \
-d "{
\"id\": \"$FILE_ID\",
\"model\": \"$MODEL_ID\",
\"chunk_duration\": 60,
\"chunk_overlap_duration\": 10
}"curl -fsS -X POST "$BASE_URL/v1/generate_text_embeddings" \
-H "Content-Type: application/json" \
-d "{\"text_input\":\"a forklift moving pallets\",\"model\":\"${MODEL_ID}\"}"Live 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/add is a placeholder — it must be overridden on the embed request or you get 400 BadParameter: "chunk_duration must be greater than 0".
POST /v1/streams/add does not deduplicate by liveStreamUrl — submitting the same URL twice mints two distinct stream_ids. Before adding, call GET /v1/streams/get-stream-info and reuse any existing registration for that URL to avoid orphaned entries.
STREAM_ID=$(curl -fsS -X POST "$BASE_URL/v1/streams/add" \
-H "Content-Type: application/json" \
-d '{"streams":[{"liveStreamUrl":"rtsp://host:port/live/video","description":"camera-001"}]}' \
| jq -r '.results[0].id')
curl -N -X POST "$BASE_URL/v1/generate_video_embeddings" \
-H "Content-Type: application/json" \
-H "Accept: text/event-stream" \
-d "{
\"id\": \"$STREAM_ID\",
\"model\": \"$MODEL_ID\",
\"stream\": true,
\"chunk_duration\": 10,
\"chunk_overlap_duration\": 2
}"
# List registered live streams (use this to recover stream_ids across sessions).
curl -fsS "$BASE_URL/v1/streams/get-stream-info"
# Stop embedding for the stream when done (terminates SSE with data: [DONE]).
curl -fsS -X DELETE "$BASE_URL/v1/generate_video_embeddings/$STREAM_ID"See references/rest-api.md for the full endpoint catalog, SSE streaming, and single-stream control-plane patterns.
docker compose -f rtvi-embed-docker-compose.yml ps
docker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed
docker stats vss-rtvi-embed
curl -fsS "$BASE_URL/v1/metrics" # Prometheus.
curl -fsS "$BASE_URL/v1/assets/stats" # Asset storage counts and TTL.If RTVI_EMBED_LOG_DIR is bound to a host directory, log files are also available at /opt/nvidia/rtvi/log/rtvi/ on the host.
:${RTVI_EMBED_PORT} (POST /v1/files, POST /v1/generate_text_embeddings, POST /v1/generate_video_embeddings, live-stream control endpoints).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).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).references/integrate-vss-deploy-video-embedding.md documents the full integration contract.
API failures return JSON with code and message fields:
{
"code": "BadParameter",
"message": "chunk_duration must be greater than 0"
}Pydantic / OpenAPI validation failures use HTTP 422 with code: "InvalidParameters" and a field-level message.
| Code | Meaning | Common Cause |
|---|---|---|
| 400 | Bad Request | Missing text_input; unknown file_id / stream_id / model; live stream called without stream: true; chunk_duration: 0 on a live-stream embed request; chunk_overlap_duration >= chunk_duration |
| 401 | Unauthorized | Missing or invalid Authorization: Bearer <token> when the deployment enforces auth |
| 403 | Forbidden | file:// URLs disabled (FILE_URL_ALLOWED_DIRS unset) or resolved path outside the allow-list (code: "Forbidden") |
| 409 | Conflict | DELETE /v1/files/{file_id} while the file is in use (ResourceInUse); another client already connected to the same live stream (Conflict) |
| 413 | Payload Too Large | Uploaded file or decoded data: URI exceeds server size limits |
| 422 | Unprocessable Entity | Schema validation failure — malformed UUID, wrong multipart field types, invalid enum values; invalid URL format for supported schemes |
| 429 | Rate Limited | Request rate exceeded — retry with exponential backoff |
| 500 | Internal Server Error | Unexpected inference or I/O failure — inspect docker compose -f rtvi-embed-docker-compose.yml logs -f rtvi-embed |
| 503 | Service Unavailable | /v1/ready still warming up (model download / Triton repo build); embedding endpoint busy with another file or text query; max live streams reached; CUDA OOM during inference |
503 on /v1/ready 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.
503 on embedding endpoints with message "Server is busy processing another file or text" or "Server is busy processing another file / live-stream." means the service handles one synchronous embed job at a time — retry with backoff or shard work across instances.
For endpoint-specific constraints (live-stream SSE requirements, URL schemes, response schemas), see references/rest-api.md. For Compose startup, cache, and permission failures, see references/troubleshooting.md.
For common failure patterns and resolutions, see references/troubleshooting.md. Frequent issues:
/v1/ready 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/Kafka peers when those flags are enabled.start_period: 1200s.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).sudo prompts for a password during deploy → use sudo -n and fail fast; see references/troubleshooting.md; never retry with interactive sudo in an agent session.Pin RTVI_EMBED_IMAGE / RTVI_EMBED_TAG, pull, recreate with --profile bp_developer_search_2d, and wait for /v1/ready before cutover. Named volumes persist across image swaps.
Full steps: Upgrade & Rollback.
Stop 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.
Full steps and cache warnings: Tear Down.
| File | When to read |
|---|---|
| references/README.md | Table of contents for all reference files. |
| references/deploy-vss-deploy-video-embedding.md | Build Vision Agent deployment reference: image, GPU, storage, startup, prerequisites, known issues. |
| references/integrate-vss-deploy-video-embedding.md | Build Vision Agent integration reference: peers, inputs/outputs, env vars, network, example Compose snippet. |
| references/rest-api.md | Full REST endpoint catalog with worked curl examples for file uploads, video/text embeddings, live streams, and health/metrics. |
| references/environment.md | Complete environment-variable matrix, including host-to-container renames and secret-sensitive variables. |
| references/troubleshooting.md | Operational diagnostics for startup, model/cache, runtime, and observability issues. |
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 11 other files (references) in skills/vss-deploy-video-embedding of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Vss Deploy Video Embedding next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Vss Deploy Video Embedding this skillNVIDIA/skills | 3.5k | — | ~3.7k | Automated safety check: Notes | Apache-2.0 | |
| Vss Deploy Video EmbeddingNVIDIA-AI-Blueprints/video-search-and-summarization | 1.9k | — | ~2.3k | Automated safety check: Notes | Apache-2.0 | |
| Create Environmentgodatadriven/whirl | 205 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Proto Backend Moduleaide-family/moon | 253 | — | ~4.1k | Automated safety check: Pass | None | |
| Use Sealoshashgraph-online/awesome-codex-plugins | 1.2k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT |
NVIDIA-AI-Blueprints/video-search-and-summarization
A skill your agent uses when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice.
godatadriven/whirl
Create a new Whirl environment in the envs/ directory. An agent skill from godatadriven/whirl.
aide-family/moon
Implements backend modules from proto definitions for goddess, marksman, and rabbit apps.
hashgraph-online/awesome-codex-plugins
Deploy and operate apps on Sealos Cloud: sign in to a Sealos account, deploy any project or self-hosted app (from the template store, an official Docker image, or project source code), provision…
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
aide-family/moon
Reviews code for correctness and potential bugs, pinpoints bug locations by file and line, and suggests concrete fixes.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Categories
A skill your agent uses when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice. Vss Deploy Video Embedding is an agent skill from NVIDIA/skills, published by the product's own GitHub organization.2 GA RT-Embed Video Embedding microservice.
Vss Deploy Video Embedding fits situations like: integrating the VSS 3.2 GA RT-Embed Video Embedding microservice; tasks that involve Embeddings.
Run `npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a claude-code`. Or copy the skill folder (skills/vss-deploy-video-embedding in NVIDIA/skills) into .claude/skills/vss-deploy-video-embedding in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a codex`. Or copy the skill folder (skills/vss-deploy-video-embedding in NVIDIA/skills) into .agents/skills/vss-deploy-video-embedding in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill vss-deploy-video-embedding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vss-deploy-video-embedding, .gemini/skills/vss-deploy-video-embedding, .github/skills/vss-deploy-video-embedding and .opencode/skills/vss-deploy-video-embedding in your project.
Going by SKILL.md and its folder, Vss Deploy Video Embedding needs the command-line tools its instructions call (curl, docker and jq) and credentials named HF_TOKEN and NGC_API_KEY. Our summary lists: Docker; A credential in NGC_API_KEY.
SKILL.md contains no URLs. Its commands use curl and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Vss Deploy Video Embedding is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Vss Deploy Video Embedding: Vss Deploy Video Embedding (NVIDIA-AI-Blueprints/video-search-and-summarization, 1.9k stars), Create Environment (godatadriven/whirl, 205 stars), Proto Backend Module (aide-family/moon, 253 stars) and Use Sealos (hashgraph-online/awesome-codex-plugins, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.