SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
Agent skill
Rtvi Byom Porting
by NVIDIA-AI-Blueprints in NVIDIA-AI-Blueprints/video-search-and-summarization
A skill your agent uses when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and…
Install Rtvi Byom Porting
$ npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill rtvi-byom-porting -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization rtvi-byom-porting --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/rtvi-byom-porting .claude/skills/rtvi-byom-porting && 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 "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .claude/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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/rtvi-byom-portingType 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 rtvi-byom-porting -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization rtvi-byom-porting --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/rtvi-byom-porting .agents/skills/rtvi-byom-porting && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .agents/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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 rtvi-byom-porting -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization rtvi-byom-porting --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/rtvi-byom-porting .cursor/skills/rtvi-byom-porting && 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 "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .cursor/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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/rtvi-byom-porting--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 rtvi-byom-porting -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization rtvi-byom-porting --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/rtvi-byom-porting .gemini/skills/rtvi-byom-porting && 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 "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .gemini/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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 rtvi-byom-portingInstalls 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 rtvi-byom-porting -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/rtvi-byom-porting .github/skills/rtvi-byom-porting && 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 "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .github/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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 rtvi-byom-porting -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 rtvi-byom-porting --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/rtvi-byom-porting .opencode/skills/rtvi-byom-porting && 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 "rtvi-byom-porting" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/deployment/rtvi-byom-porting into .opencode/skills/rtvi-byom-porting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rtvi-byom-porting", 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
rtvi-byom-porting- GitHub stars
- 1.9k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 635 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and…
- Works in 4 steps: Configuration only: use… → RTVI adapter: normalize config,… → Plugin or shim: register architecture… → …
- Validating a bring-your-own VLM in VSS RT-VLM
- SKILL.md covers Purpose, Model Contract, Integration Decision and Hard Gates, plus 2 more sections
- Runs Python scripts from its folder; calls python3; needs HF_TOKEN
What it does
Rtvi Byom Porting is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and model-specific runtime dependencies. Not for selecting an already-supported model or ordinary RT-VLM deployment.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/quality-gates.md` and `references/vllm-porting.md`).
It sits in AI & LLM Engineering, covering LLM inference and serving, Model hubs and datasets and Deployment. It works with vLLM and Hugging Face. 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
- Validating a bring-your-own VLM in VSS RT-VLM
- Including custom Hugging Face
- NGC checkpoints
- Model-specific runtime dependencies
Example prompts
- “/rtvi-byom-porting”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Configuration only: use VLM_MODEL_TO_USE=vllm-compatible and
- RTVI adapter: normalize config, processor, request or response behavior
- Plugin or shim: register architecture and deterministic weight mappings
- Version-gated patch: patch vLLM only after recording why the first three
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
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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:
HF_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Rtvi Byom Porting loads about 1.4k tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 635 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 passed
The automated check found no risky patterns in SKILL.md.
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); the scripts in this folder 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). 635 words, ~1,398 tokens.
Download SKILL.mdSave it as
.claude/skills/rtvi-byom-porting/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- rtvi-byom-porting
- description
- Use when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and model-specific runtime dependencies. Not for selecting an already-supported model or ordinary RT-VLM deployment.
- 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 rtvi vlm byom vllm model-porting
RTVI BYOM Porting
Purpose
Port a VLM into the VSS RT-VLM service without hard-coding one checkpoint or GPU platform. Prefer configuration, then an RTVI adapter or vLLM plugin, and patch vLLM only when the supported extension points cannot express the model.
Use vss-deploy-dense-captioning for ordinary deployment or an already-supported
model. Use this skill when model architecture, processor, weight mapping, runtime
dependencies, or request adaptation requires repository work.
Model Contract
Before changing code, record:
- immutable model source and revision, license and access requirements;
- architecture, processor, tokenizer, quantization and context length;
- vision-token and frame-sampling assumptions;
- CUDA architecture, memory, vLLM/runtime and custom-kernel requirements.
For a private Hugging Face source, use HF_TOKEN only during authenticated
download/cache population. Never print it, embed it in MODEL_PATH, or bake it
into an image. Prove the cached model starts after the temporary credential is
removed. If remote model code is required, review it first and pair
VLM_TRUST_REMOTE_CODE=true with an exact allowlist entry. For the VSS profile,
set RTVI_VLM_MODEL_PATH_ALLOWLIST; inside the service container this becomes
RTVI_MODEL_PATH_ALLOWLIST. Likewise, the profile input
RTVI_VLM_ALLOW_UNSAFE_MODEL_CONFIG becomes
RTVI_ALLOW_UNSAFE_MODEL_CONFIG; do not enable it without reviewing the
blocked config hooks.
Integration Decision
Stop at the first path that works:
- Configuration only: use
VLM_MODEL_TO_USE=vllm-compatibleandMODEL_PATH=<ngc: or mounted path>. Agit:source is acceptable only for exploration because the develop downloader does not pin Hugging Face revisions; use a revision-pinned mounted snapshot for reproducible evidence. - RTVI adapter: normalize config, processor, request or response behavior
inside
services/rtvi/rt-vlm/while preserving existing model behavior. - Plugin or shim: register architecture and deterministic weight mappings without editing vendored vLLM internals.
- Version-gated patch: patch vLLM only after recording why the first three paths cannot work. Keep it optional and add a focused regression test.
Use VLM_MODEL_TO_USE=custom with MODEL_IMPLEMENTATION_PATH only for a custom
RTVI model implementation. In the VSS Compose profile these are exposed as
RTVI_VLM_MODEL_TO_USE, RTVI_VLM_MODEL_PATH, and
RTVI_VLM_MODEL_IMPLEMENTATION_PATH.
For source, adapter, plugin, or custom-backend changes, build the RT-VLM image
from services/rtvi/rt-vlm/, test it with standalone Compose via RTVI_IMAGE,
then select the same repository and tag in the VSS profile via
VSS_RT_VLM_IMAGE and VSS_RT_VLM_TAG. Record the tested image digest. A host
MODEL_IMPLEMENTATION_PATH alone is insufficient: the implementation must
exist at that path inside the selected image or an explicit Compose bind mount.
Read references/vllm-porting.md before changing model loading, registration, weight mapping, kernels, cache behavior or vLLM.
Show full SKILL.md (249 more words)Show less
Hard Gates
- Do not enable eager mode by default. If it is unavoidable, capture the exact blocker, performance impact and removal condition.
- Do not bind generic model logic to H100, B200, RTX, Orin, x86 or Jetson. Capability-detect or use existing configuration boundaries.
- Keep secrets out of commands, logs, images, reports and committed files.
- Do not accept a successful load as proof of a successful port. Output must be legible and grounded for every claimed modality.
Validation
Run the smallest relevant checks in this order:
- Static checks and focused tests for changed Python, shell and configuration.
- Start RT-VLM through the canonical VSS Compose/profile path and verify
/v1/health/readyand/v1/models. First use standalone Compose when a revision-pinned host model snapshot must be mounted; the VSS profile does not exposeMODEL_ROOT_DIRon develop. - Smoke-test text, image and video inputs for every claimed modality. Set the explicit media type when testing images rather than relying on video routing.
- Read references/quality-gates.md, retain raw redacted responses, and reject gibberish, repetition or ungrounded claims.
- Run relevant caption, event-detection or summarization accuracy checks.
- Record latency, throughput, GPU memory/utilization, eager state and custom kernel state.
Use skills/deployment/rtvi-byom-porting/scripts/byom_port_report.py to turn
observed JSON facts into a compact Markdown report. Evidence is mandatory for a
PASS result; the helper does not manufacture it. Run
python3 skills/deployment/rtvi-byom-porting/scripts/tests/test_byom_port_report.py
after changing the helper.
Completion Contract
Report the exact model revision and backend, integration path, eager-mode state, platform gating, smoke/accuracy/performance evidence, remaining blockers and the next bounded validation step.
© 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 5 other files (scripts, references) in skills/deployment/rtvi-byom-porting of NVIDIA-AI-Blueprints/video-search-and-summarization.
- SKILL.md
- agents/openai.yaml
- references/quality-gates.md
- references/vllm-porting.md
- scripts/byom_port_report.py
- scripts/tests/test_byom_port_report.py
Open the folder on GitHubat commit fdb6a7a
Compare with similar skills
Rtvi Byom Porting 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 |
|---|---|---|---|---|---|---|
| Rtvi Byom Porting this skillNVIDIA-AI-Blueprints/video-search-and-summarization | 1.9k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hf Cloud Serving Image Selectionwaybarrios/opencode-power-pack | 534 | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Add Modelguoqingbao/xinfer | 334 | — | ~4.2k | Automated safety check: Notes | MIT | |
| Resolvealexziskind1/model-shelf | 130 | — | ~792 | Automated safety check: Pass | MIT |
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- Official
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Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
Add Model
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Adapt and port new LLM model architectures to this xinfer project.
Resolve
alexziskind1/model-shelf
Always resolve Hugging Face models via model-shelf before any download.
Check Model
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Check model compatibility with xinfer before loading. An agent skill from guoqingbao/xinfer.
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Questions about Rtvi Byom Porting
What does Rtvi Byom Porting do?
A skill your agent uses when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and…. Rtvi Byom Porting is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use when adding, debugging, or validating a bring-your-own VLM in VSS RT-VLM, including custom Hugging Face or NGC checkpoints, vLLM adapters or plugins, model shims, and model-specific runtime dependencies.
When should I use Rtvi Byom Porting?
Rtvi Byom Porting fits situations like: validating a bring-your-own VLM in VSS RT-VLM; including custom Hugging Face; NGC checkpoints; model-specific runtime dependencies.
How do I install Rtvi Byom Porting in Claude Code?
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill rtvi-byom-porting -a claude-code`. Or copy the skill folder (skills/deployment/rtvi-byom-porting in NVIDIA-AI-Blueprints/video-search-and-summarization) into .claude/skills/rtvi-byom-porting in your project. Claude Code loads it when a task matches its description.
How do I install Rtvi Byom Porting in Codex?
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill rtvi-byom-porting -a codex`. Or copy the skill folder (skills/deployment/rtvi-byom-porting in NVIDIA-AI-Blueprints/video-search-and-summarization) into .agents/skills/rtvi-byom-porting in your project. Codex loads it when a task matches its description.
Can I use Rtvi Byom Porting 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 rtvi-byom-porting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rtvi-byom-porting, .gemini/skills/rtvi-byom-porting, .github/skills/rtvi-byom-porting and .opencode/skills/rtvi-byom-porting in your project.
What does Rtvi Byom Porting need to run?
Going by SKILL.md and its folder, Rtvi Byom Porting needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named HF_TOKEN. Our summary lists: Python 3.
Does Rtvi Byom Porting 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 Rtvi Byom Porting safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Rtvi Byom Porting use?
Rtvi Byom Porting 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 Rtvi Byom Porting use?
About 1.4k tokens (SKILL.md is roughly 5.6k 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 1.8k tokens, read only when the agent opens those files.
What are the alternatives to Rtvi Byom Porting?
Skills that share tags, products or a category with Rtvi Byom Porting: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hf Cloud Serving Image Selection (waybarrios/opencode-power-pack, 534 stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars) and Add Model (guoqingbao/xinfer, 334 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Rtvi Byom Porting?
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.