Vss Deploy Video Embedding
NVIDIA/skills
A skill your agent uses when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice.
Agent skill
Vss Deploy Video Embedding
by NVIDIA-AI-Blueprints in 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.
Install Vss Deploy Video Embedding
$ npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization.git --path skills/deployment/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-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization --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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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-AI-Blueprints/video-search-and-summarization --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-AI-Blueprints/video-search-and-summarization 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-AI-Blueprints/video-search-and-summarization.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/deployment/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-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/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.
Facts
- Skill name
vss-deploy-video-embedding- GitHub stars
- 1.9k
- Token cost
- ~2.3k tokens
- SKILL.md length
- 847 words
- Files
- 14 (incl. references)
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice.
- Customizing the VSS RT-Embed Video Embedding microservice
- SKILL.md covers When to Use, Service Snapshot, Route First and Operating Rules, plus 2 more sections
- Needs NGC_API_KEY and HF_TOKEN
- General VSS deployment work that does not include RT-Embed
What it does
Vss Deploy Video Embedding is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use this skill when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice. Covers standalone Docker Compose deployment, the /v1 REST API for text/video embeddings and live streams, Redis/Kafka/OTel integration, troubleshooting, and bring-your-own-model (BYOM) custom embedding backends, with VideoPrism as an example. Do not use for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work that does not include RT-Embed.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `BENCHMARK.md`, `evals/byom_custom_model_workflow.json` and `evals/byom_routing.json`).
It sits in AI & LLM Engineering, covering Embeddings and Deployment. It works with Redis, Apache Kafka, OpenTelemetry and Docker. The repository describes itself as: NVIDIA AI Blueprint for video search and summarization (VSS) is a GPU-accelerated reference architecture for building video analytics agents with real-time verified alerts… The licence is Apache-2.0.
When your agent uses it
- Customizing the VSS RT-Embed Video Embedding microservice
- General VSS deployment work that does not include RT-Embed
Example prompts
- “/vss-deploy-video-embedding”
Requirements
- Docker
- A credential in NGC_API_KEY
What it can do on your machine
Read from SKILL.md and the folder at commit fdb6a7a. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
NGC_API_KEYHF_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Vss Deploy Video Embedding loads about 2.3k tokens when it runs, and up to ~22k if it reads all its reference files. Until then it costs about 123 tokens; SKILL.md has 847 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.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteRuns commands with sudoSKILL.md:100
`sudo -n docker` and stop with the exact manual command if passwordless sudo is
Automated 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.
SKILL.md
The full file from NVIDIA-AI-Blueprints/video-search-and-summarization at commit fdb6a7a, republished under its Apache-2.0 licence (© NVIDIA-AI-Blueprints). 847 words, ~2,286 tokens.
Download SKILL.mdSave it as
.claude/skills/vss-deploy-video-embedding/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.- name
- vss-deploy-video-embedding
- description
- Use this skill when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice. Covers standalone Docker Compose deployment, the `/v1` REST API for text/video embeddings and live streams, Redis/Kafka/OTel integration, troubleshooting, and bring-your-own-model (BYOM) custom embedding backends, with VideoPrism as an example. Do not use for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work that does not include RT-Embed.
- license
- Apache-2.0
- metadata.version
- 3.3.0-rc0
- metadata.github-url
- https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization
- metadata.tags
- nvidia blueprint operational deployment byom rtvi-embed videoprism
VSS Video Embedding (RT-Embed)
Use this skill for the RT-Embed video embedding microservice, including the standard Cosmos-Embed1 deployment path and custom/BYOM embedding model work.
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,
RT-Embed BYOM, VideoPrism embed, custom embed model,
MODEL_IMPLEMENTATION_PATH, MODEL_REPOSITORY_SCRIPT_PATH, bring your own embedding model.
Do not use this skill for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work unless the request deploys, operates, integrates, or customizes RT-Embed.
When to Use
- Deploy, size, upgrade, roll back, or tear down standalone RT-Embed (Cosmos-Embed1 or a custom/BYOM model)
- Call RT-Embed's
/v1API for text/video embeddings, live streams, model listing, health, metrics - Wire RT-Embed into another service with Redis, Kafka, OpenTelemetry, auth, or storage
- Add or validate a custom/BYOM embedding backend
- Debug RT-Embed readiness, model/cache startup, or Redis/Kafka reachability
Service Snapshot
- Skill:
vss-deploy-video-embedding. - Legacy 3.1 name: RT-Embed.
- Compose service:
rtvi-embed. - Container name:
vss-rtvi-embed. - Image:
ghcr.io/nvidia-ai-blueprints/vss/vss-rt-embed(override withVSS_RT_EMBED_IMAGE). - Default tag:
develop-latest(override withVSS_RT_EMBED_TAG; usedevelop-latest-sbsafor an SBSA/DGX Spark host). - Profile:
rtvi-embed. - Container port:
8000(host-side${RTVI_EMBED_PORT}). - Default model:
cosmos-embed1-448pfromnvidia/Cosmos-Embed1-448p. - BYOM loader variables:
MODEL_PATH,MODEL_IMPLEMENTATION_PATH,MODEL_REPOSITORY_SCRIPT_PATH. - Health endpoint:
GET /v1/ready. - Healthcheck startup grace:
1200s(20 minutes) on first boot.
Route First
Choose one primary path before acting. Load the linked reference and follow it; do not duplicate full workflows from this top-level file.
| User intent | Use this path |
|---|---|
| Deploy, size, upgrade, roll back, or tear down standalone RT-Embed with the default Cosmos-Embed1 model | references/deploy-vss-deploy-video-embedding.md |
| Call RT-Embed APIs for files, text/video embeddings, live streams, model listing, health, metrics, metadata, or manifests | references/rest-api.md |
| Wire RT-Embed into another service or deployment with Redis, Kafka, OpenTelemetry, auth, storage, or env var mapping | references/integrate-vss-deploy-video-embedding.md and references/environment.md |
| Use decoded-frame IPC from a compatible RTVI CV producer | references/environment.md#decoded-frame-ipc and references/integrate-vss-deploy-video-embedding.md |
| Add, wire, or validate a custom/BYOM embedding backend, with VideoPrism as an example | references/byom-custom-model.md |
| Debug readiness, model/cache startup, permissions, Redis/Kafka reachability, API failures, or observability | references/troubleshooting.md |
Selection rules:
- If asked which skill handles default Cosmos-Embed1 RT-Embed deployment,
answer: use
vss-deploy-video-embedding; this is the default deployment path, not the BYOM/custom-model path. - For normal RT-Embed or Cosmos-Embed1 deployment, use the deployment reference. In the answer, explicitly say that this is the default RT-Embed deployment path. Also explicitly distinguish it from BYOM/custom model integration: BYOM is only for adding or validating non-default custom embedding backends such as VideoPrism, and is not needed for the default Cosmos-Embed1 model.
- For BYOM, custom embedding models, VideoPrism examples, or model implementation path questions, use the BYOM reference first, then deployment/API references only as needed.
- For direct endpoint calls, use the API reference and reuse deployment context only when the service is not already running.
- Decoded-frame IPC requires a compatible RTVI CV producer on the same host, a
shared socket directory accessible to UID/GID
1001, and a matching camera ID. The consumer uses the fixed/run/rtvi-ipc/nvds_ipc_{camera_id}.socksocket contract. IPC camera, sensor, and stream IDs must be non-empty and contain only ASCII letters, digits,.,_, and-; standard UUIDs are valid. It applies only to live RTSP processing. - If the request mixes deployment and BYOM, load BYOM first to establish model path requirements, then use the deployment reference to run the service.
Show full SKILL.md (316 more words)Show less
Operating Rules
- Do not deploy a full VSS profile for standalone RT-Embed. Work from
deploy/docker/services/rtvi/rtvi-embedunless the user explicitly asks for a profile deployment. - Never let
sudoprompt interactively. Prefer plaindocker; otherwise usesudo -n dockerand stop with the exact manual command if passwordless sudo is unavailable. - Do not expose full values of
NGC_API_KEY,HF_TOKEN, bearer tokens, or model repository credentials in prompts, logs, or final answers. - Do not shorten the
start_period: 1200shealthcheck during first boot. Cosmos model download and Triton model repository generation can take up to 20 minutes. - In standalone mode, disable missing peers with
MESSAGE_BUS=,ERROR_BUS=, andENABLE_REDIS_ERROR_MESSAGES=falseunless the corresponding Kafka or Redis service is started and reachable. - For BYOM models that are video-only, require an explicit text endpoint decision:
either a compatible text encoder in the same embedding space or a clear 4xx
response for
/v1/generate_text_embeddings.
Quick Reference
- Deployment details: image, GPU, storage, startup, readiness, upgrade, and
teardown live in
references/deploy-vss-deploy-video-embedding.md. - API details: file upload, text/video embeddings, live-stream control,
models, health, metadata, and metrics live in
references/rest-api.md. - Integration details: inputs/outputs, Redis/Kafka/OTel, auth, networking,
and Compose snippets live in
references/integrate-vss-deploy-video-embedding.md. - Environment matrix: host-to-container renames, optional volumes, and
secret-sensitive variables live in
references/environment.md. - BYOM details: custom model contract, Docker/Helm overrides, model path
variables, and VideoPrism example validation live in
references/byom-custom-model.md. - Troubleshooting details: common startup, cache, permission, bus, and API
failures live in
references/troubleshooting.md.
References
| File | When to read |
|---|---|
| references/README.md | Table of contents for all reference files. |
| references/deploy-vss-deploy-video-embedding.md | Deployment reference: image, GPU, storage, startup, prerequisites, known issues. |
| references/rest-api.md | Full REST endpoint catalog with worked curl examples for file uploads, video/text embeddings, live streams, and health/metrics. |
| references/integrate-vss-deploy-video-embedding.md | Integration reference: peers, inputs/outputs, env vars, network, example Compose snippet. |
| references/environment.md | Complete environment-variable matrix, including host-to-container renames and secret-sensitive variables. |
| references/byom-custom-model.md | BYOM reference: custom model contract, path overrides, Docker/Helm wiring, and VideoPrism example validation checklist. |
| references/troubleshooting.md | Operational diagnostics for startup, model/cache, runtime, and observability issues. |
© NVIDIA-AI-Blueprints, 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
Files
SKILL.md and 13 other files (references) in skills/deployment/vss-deploy-video-embedding of NVIDIA-AI-Blueprints/video-search-and-summarization.
- SKILL.md
- BENCHMARK.md
- evals/byom_custom_model_workflow.json
- evals/byom_routing.json
- evals/evals.json
- evals/standalone_deploy.json
- references/README.md
- references/byom-custom-model.md
- references/deploy-vss-deploy-video-embedding.md
- references/environment.md
- references/integrate-vss-deploy-video-embedding.md
- references/rest-api.md
- references/troubleshooting.md
- skill-card.md
Open the folder on GitHubat commit fdb6a7a
Compare with similar skills
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-AI-Blueprints/video-search-and-summarization | 1.9k | — | ~2.3k | Automated safety check: Notes | Apache-2.0 | |
| Vss Deploy Video EmbeddingNVIDIA/skills | 3.6k | — | ~3.7k | Automated safety check: Notes | Apache-2.0 | |
| Monstermq Broker Configvogler75/monster-mq | 143 | — | ~2.2k | Automated safety check: Pass | GPL-3.0 | |
| 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 | |
| Vss Deploy Dense CaptioningNVIDIA/skills | 3.6k | 1 repos | ~3.3k | Automated safety check: Notes | Apache-2.0 |
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Questions about Vss Deploy Video Embedding
What does Vss Deploy Video Embedding do?
A skill your agent uses when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice. Vss Deploy Video Embedding is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use this skill when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice.
When should I use Vss Deploy Video Embedding?
Vss Deploy Video Embedding fits situations like: customizing the VSS RT-Embed Video Embedding microservice; general VSS deployment work that does not include RT-Embed.
How do I install Vss Deploy Video Embedding in Claude Code?
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a claude-code`. Or copy the skill folder (skills/deployment/vss-deploy-video-embedding in NVIDIA-AI-Blueprints/video-search-and-summarization) into .claude/skills/vss-deploy-video-embedding in your project. Claude Code loads it when a task matches its description.
How do I install Vss Deploy Video Embedding in Codex?
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -a codex`. Or copy the skill folder (skills/deployment/vss-deploy-video-embedding in NVIDIA-AI-Blueprints/video-search-and-summarization) into .agents/skills/vss-deploy-video-embedding in your project. Codex loads it when a task matches its description.
Can I use Vss Deploy Video Embedding in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --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.
What does Vss Deploy Video Embedding need to run?
Going by SKILL.md and its folder, Vss Deploy Video Embedding needs credentials named NGC_API_KEY and HF_TOKEN. Our summary lists: Docker; A credential in NGC_API_KEY.
Does Vss Deploy Video Embedding access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Vss Deploy Video Embedding safe to install?
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.
What licence does Vss Deploy Video Embedding use?
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.
How many tokens does Vss Deploy Video Embedding use?
About 2.3k tokens (SKILL.md is roughly 9.1k 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 19k tokens, read only when the agent opens those files.
What are the alternatives to Vss Deploy Video Embedding?
Skills that share tags, products or a category with Vss Deploy Video Embedding: Vss Deploy Video Embedding (NVIDIA/skills, 3.6k stars), Monstermq Broker Config (vogler75/monster-mq, 143 stars), Create Environment (godatadriven/whirl, 205 stars) and Proto Backend Module (aide-family/moon, 253 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Vss Deploy Video Embedding?
NVIDIA-AI-Blueprints (a GitHub organization) maintains it in NVIDIA-AI-Blueprints/video-search-and-summarization, which has 1,919 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 10, 2026.
Source: NVIDIA-AI-Blueprints/video-search-and-summarization on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.