A skill your agent uses when summarizing a recorded video through HITL-gated LVS, falling back to vss vlm run when LVS is not ready.

Apache-2.0Auto-check: warningsAI & LLM Engineering

Install Vss Summarize Video

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-summarize-video -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization vss-summarize-video --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/operations/vss-summarize-video .claude/skills/vss-summarize-video && rm -rf skills-src

Use ~/.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/

Facts

Skill name
vss-summarize-video
GitHub stars
1.9k
Token cost
~5.3k tokens
SKILL.md length
2,825 words
Files
15 (incl. references, assets)
Skills in repo
22
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when summarizing a recorded video through HITL-gated LVS, falling back to vss vlm run when LVS is not ready.

  • Works in 5 steps: Select the Backend → Prepare the Video Through VIOS → Collect LVS Settings → …
  • Summarizing a recorded video through HITL-gated LVS
  • SKILL.md covers Instructions, Examples, When to Use and Required References, plus 9 more sections
  • Calls uv, docker and kubectl

What it does

Vss Summarize Video is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use when summarizing a recorded video through HITL-gated LVS, falling back to vss vlm run when LVS is not ready. Not for reports, archive search, or live RTSP captioning.

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including reference files and assets (for example `evals/evals.json`, `evals/lvs_api_ops.json` and `evals/lvs_profile_summarize.json`).

It sits in AI & LLM Engineering, covering Summarization. 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

  • Summarizing a recorded video through HITL-gated LVS
  • Falling back to vss vlm run when LVS is not ready

Example prompts

  • “/vss-summarize-video”

Requirements

  • Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Select the Backend
  2. Prepare the Video Through VIOS
  3. Collect LVS Settings
  4. Submit Once Through the CLI
  5. Present the Result

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

    Shell commands in SKILL.md call:

    • uv
    • docker
    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, docker and kubectl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vss Summarize Video loads about 5.3k tokens when it runs, and up to ~25k if it reads all its reference files. Until then it costs about 48 tokens; SKILL.md has 2,825 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~25k

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: warnings

The automated check found patterns that need a careful read before installing.

  • WarningContains instruction-override wording (e.g. “without asking the user”)SKILL.md:33
    - Run API commands yourself; do not tell the user to run them.

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). 2,825 words, ~5,303 tokens.

Download SKILL.mdSave it as .claude/skills/vss-summarize-video/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
vss-summarize-video
description
Use when summarizing a recorded video through HITL-gated LVS, falling back to `vss vlm run` when LVS is not ready. Not for reports, archive search, or live RTSP captioning.
license
Apache-2.0
metadata.version
3.3.0-rc0
metadata.author
NVIDIA Video Search and Summarization team
metadata.github-url
https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization
metadata.tags
nvidia blueprint operational
metadata.vss-requires
summarize|vlm

VSS Summarize Video

Instructions

  • Before readiness checks or video preparation, reject summarization of any stream (RTSP/RTSPS URL or registered camera), including recorded time windows: Live-stream summarization / report generation isn't supported. Then stop; do not ingest, extract clips, invoke inference/fallback, or deploy captioning. Reject an explicitly identified stream/camera even if its URL is absent. For a registered sensor name/id (not a local file path, uploaded file, or direct recorded-video URL), first check its current type with only vss vios list --sensor <name>; never reuse an earlier file classification. Reject a stream; if the type is unclear or the lookup fails, report that limitation and stop. Local/uploaded files follow the recorded-video workflow, including Stage 2 registration when absent; do not classify their paths as sensors.
  • Execute the five workflow stages below in order.
  • Run API commands yourself; do not tell the user to run them.
  • Use the required references at their named decision points.

Examples

Runnable scenarios live under evals/. The command implementations are in references/end-to-end-example.md.

When to Use

Use when summarizing a recorded video through HITL-gated LVS, to produce one polished narrative summary with timestamped events when LVS is available.

Do not use this skill for:

  • Live RTSP captioning: use vss-deploy-dense-captioning.
  • Incident or alert-window reports: use vss-generate-video-report Mode B.
  • Archive search: use vss-search-archive.

Required References

Load these files only as directed:

Core Invariants

  • Route by LVS readiness, never by video duration.
  • HTTP 200 from /v1/ready selects LVS. Empty response bodies do not mean unavailable.
  • Once LVS is selected, do not call a VLM /v1/chat/completions endpoint.
  • Issue exactly one vss summarize run per recorded segment. One run is one POST /v1/summarize. Never retry, hedge, broaden events, or run a second backend automatically.
  • Endpoints come from the deployment vss configure recorded. Never pass an endpoint, index, or model flag the caller did not name, and never replace a failed run with hand-rolled curl against /v1/summarize.
  • Save the complete command and its stdout. Diagnose failures from those files, the run's own exit code, service logs, and non-mutating GET requests.
  • Render video_summary and every returned event verbatim. Do not paraphrase, truncate descriptions, add fields, or fabricate id.
  • When LVS is not ready, fall back to vss vlm run directly. Do not ask first, and do not offer to deploy LVS.

Prerequisites

  • The lvs profile, reachable through the origin recorded by vss configure.
  • curl for the readiness probe only, and jq for reading CLI JSON. Capture stdout before piping it, or use set -o pipefail. Exit codes and the common CLI rules live in the repository root AGENTS.md.
  • Network reachability from the LVS service to the final VIOS clip URL (Docker: from vss-lvs; Kubernetes: deploy must mint a URL the LVS pod can fetch).
  • The vss CLI on PATH. The OpenClaw and Hermes harness images ship it; anywhere else, install it from the same checkout as this skill so the CLI and the skill match: uv tool install <checkout>/libs/vss/cli.
  • One recorded deployment origin:
bash
vss summarize run --help >/dev/null || exit 1
vss configure show

vss configure show fails when nothing is recorded. Then the only setup is vss configure --base-url "${VSS_PUBLIC_URL}", with the ingress origin the operator gave you. If VSS_PUBLIC_URL is unset, stop and ask for that origin; do not substitute HOST_IP, localhost, or a port.

Configure against the ingress origin, never :38111 — that LVS container port exposes no Elasticsearch, so a deployment recorded from it cannot persist.

The vss-build-vision-ai skill can deploy the profile.

Limitations

  • Direct VLM fallback cannot target LVS scenarios or events and is lower quality.
  • Private VIOS URLs may be unreachable from remote VLM endpoints.
  • One vss summarize run per recorded segment, with no automatic retry.
  • Persistence needs a routed Elasticsearch. A deployment without one summarizes and reports the result unpersisted rather than failing the job.
  • Both edges are configured to wait an hour, matching the CLI's own default, so a long summarization is not cut short by a 504 that would be recorded as a failed job. An Ingress the deployment overrides shorter still caps the wait.

Recorded services

Every URL the recorded-video workflow and a direct API question touch is one vss configure recorded. Read it from vss configure show; never assemble it from VSS_PUBLIC_URL, HOST_IP, or a port (the one exception is the Stage 2 rewrite of a loopback media_url host to the host's routable IP), and ignore leftover LVS_BACKEND_URL / VLM_BASE_URL / RTVI_VLM_BASE_URL. Do not use kubectl port-forward, Service DNS, NodePorts, docker exec, or docker inspect, and do not scan ports or configuration files for an endpoint.

ServiceRecorded asCalled by the workflow
LVSservices.lvs.url (the /lvs mount)only GET …/v1/ready, the readiness probe; vss summarize run resolves the rest
VLM / RT-VLMservices.rt_vlm.url (the /rtvi-vlm mount)only GET …/v1/models, a reachability check in the same probe; vss vlm run resolves the rest

The readiness probe is the only direct HTTP the workflow makes, and it exists because the CLI has no readiness verb: vss configure records LVS on liveness (/lvs/v1/live), while routing needs HTTP 200 from /v1/ready. It is not a fallback. When a vss command fails, report that failure; never repeat the work with curl against /v1/summarize or /v1/chat/completions.

Do not treat the origin's /openapi.json as the LVS schema; on stock Ingress that path is the Agent's.

Routing

Probe LVS /v1/ready using the loop in the end-to-end reference. Readiness is the HTTP status only: retry 503 warmup responses for about 30 seconds, and do not inspect the body. No recorded lvs service counts as not ready.

LVS resultAction
HTTP 200Use LVS for every video duration.
Anything elseUse the VLM fallback (vss vlm run) without asking.

Recorded Video Workflow

Stage 1: Select the Backend

Load the end-to-end and CLI references. Run the LVS readiness probe before preparing the clip.

The summarization model needs no discovery: vss configure recorded the id LVS reports serving, and vss summarize run defaults to it on both Docker and Kubernetes. The VLM fallback needs none either: vss vlm run defaults to the model the deployment's RT-VLM reports. Pass --model only when the caller named one, and read the recorded value from vss configure show when it has to be reported.

A non-200 LVS readiness result after warmup is the only unavailability signal. An empty summary, empty events, missing optional fields, or empty readiness stdout must not trigger fallback.

Stage 2: Prepare the Video Through VIOS

Use the vss CLI for every step; no VIOS REST calls, and do not invoke a separate skill.

  1. A named sensor goes straight to step 3. For a file, vss vios list --sensor <stem>; reuse the recording when present. VIOS names an uploaded sensor by its filename stem.
  2. If absent and the exact local file is available, vss vios add <file>. It waits for the timeline; its default timestamp is 2025-01-01T00:00:00.000Z.
  3. vss vios timeline --sensor <name>, then for each segment vss vios clip --sensor <name> --start-time <start> --end-time <end>. Always pass the segment's bounds: a window may not span a gap, and an RTSP sensor has no default window. Pass media_url to --url as returned, except that a localhost / 127.0.0.1 host becomes the host's routable IP: vss-lvs cannot fetch loopback and rejects it.
  4. If warmed is false, stop and report it. warmed: true shows only that the CLI host fetched the URL, not that LVS can.

Require the exact recording, full timeline, and fresh clip URL before continuing. When the source file is available, compare VIOS timeline duration with source duration.

If preparation fails, stop and report the missing prerequisite. Do not choose an arbitrary /tmp video, alternate recording, local HTTP server, NvStreamer, or RTSP source unless the user explicitly requested that source.

Stage 3: Collect LVS Settings

When LVS is selected, load the HITL reference and collect scenario, events, and optional objects_of_interest before the summarize run.

When the caller explicitly says to run autonomously without prompting and asks for defaults or supplies no settings, use these values verbatim:

text
scenario="activity monitoring"
events=["notable activity"]

This is the only HITL bypass. Do not infer defaults from filenames or sensor names. Mention defaults in the final response and offer a separate rerun with specific settings.

Show full SKILL.md (1,275 more words)Show less
Stage 4: Submit Once Through the CLI

Load the CLI reference. vss summarize run issues the summarize request on both Docker and Kubernetes, and persists only when static memory policy enables it. Do not build a /v1/summarize payload by hand, and do not fetch /openapi.json to construct one — the CLI owns the request shape, vss configure owns the endpoint, and vss configure memory owns persistence.

Use the invocation in the end-to-end reference. It passes the fresh VIOS URL from Stage 2, the exact HITL values from Stage 3, --chunk-duration 10, and --seed 1; repeat --event per event and add --object-of-interest only when the caller provided objects. Pass no endpoint flag.

Do not pass --persist or --memory-index; those per-request controls do not exist. The standard workflow also does not pass --no-persist, so the operator's configured persistence default applies. When persistence is enabled, the record needs two values:

  • --video-id, required alongside --url. Use the recording's VIOS sensor id — the sensor_id from Stage 2's vss vios clip output — never the stream id. It becomes the record's sensor, which is what list --sensor-id and time-windowed recall key on. Without --video-id the run exits 2 before summarizing rather than after.
  • --creation-time, the media's absolute start. LVS reports event times as offsets into the clip unless this anchors them, and unified memory stores instants — so without it the events cannot be written and the run degrades to exit 6. For uploaded sample media use the same 2025-01-01T00:00:00.000Z Stage 2 uploaded with.

Do not pass --num-frames-per-chunk in the standard workflow. RT-VLM owns frame sampling; unset fields are absent from the request, so the deployment's own default applies.

The final line of stdout is one JSON object naming the job. Read that line and the exit code; the prose on stderr is a diagnostic, not the result. A call refused before a job exists prints no marker at all and stderr is the whole result — check stdout is non-empty before parsing it. Emptiness, not the exit code, is what says whether a job was created.

exitmeaningaction
0summarized; persistence followed configured policypresent the result and report persisted truthfully
2a flag the CLI refused, before anything was submittedfix the call, then run once
2LVS rejected the request it was sent; the marker names a job closed as failedreport the failure with that job_id
3LVS unreachable or returned 5xxreport it with the marker's job_id
4deployment configuration is missing, or an explicit Markdown note lacks static sink configurationno job, no marker — run the remediation command from stderr
6summary produced; Elasticsearch or Markdown cache write failedpresent the summary; report ES and Markdown outcomes separately
7timed outreconcile with vss summarize get --job-id; do not re-run

Exits 6 and 7 both mean the summarization already happened. Never repeat the run to obtain a different view of it, and never repeat it for diagnosis — a second run requires a separate user request. The exit 2 that carries a marker is the same story earlier: the request reached LVS and came back refused, so the one submission this request had is spent and a corrected call belongs to a new request. Once a job exists, every outcome names its job_id; use it rather than re-running. A call refused before a job exists names nothing, which is why empty stdout is the test.

The completion marker's persisted boolean is the authoritative Elasticsearch outcome. The result body includes persist only when persistence was attempted and reports its index and event count; optional Markdown status is separate under memory_note. record says what the job_id is worth to a later read: closed, absent when policy skipped persistence, or stale when a submitted record could not be closed. Do not read the record back to confirm it, and never read Elasticsearch directly — recalling memory is a separate skill's job. The one read that belongs here is reconciling an exit 7, whose outcome is genuinely unknown until vss summarize get --job-id <job_id> answers.

If video_summary and events are empty, inspect the same payload's summary.usage.total_chunks_processed. A positive integer confirms processing; zero or missing means processing was not confirmed. Do not claim "no detections."

VLM Fallback for Stages 3-4

Use the fallback when LVS remained unavailable after warmup; do not ask first. Do not run LVS HITL, and never use fallback to repair or replace an LVS response. Run one vss vlm run --sensor <name> --start-time <start> --end-time <end> per recorded segment from vss vios timeline --sensor <name>, with the default prompt in the end-to-end reference. The CLI resolves the clip and the model itself; do not call /v1/chat/completions by hand.

Before the result, include:

Note: Input video <name> is <N>s long. The video summarization service is not deployed, so this summary was produced by the VLM alone with a generic default prompt. Deploy the lvs profile for higher-quality summaries with scenario/events targeting.

A non-zero vss vlm run exit is the result to report; do not retry it.

Stage 5: Present the Result

Start with exactly one header:

text
Summary of <video_name> (<duration>)

Use Ns below 60 seconds and Mm Ss otherwise.

For LVS, the CLI nests the service's own envelope under summary: parse the JSON string in summary.choices[0].message.content while preserving summary.usage. Render video_summary verbatim, followed by every event in service order. Preserve every returned field and the full description; use a per-event list if a table would truncate text.

Close with the job's identity: the job_id, the completion marker's persisted value, and any separate memory_note result. An absent persist object with persisted=false means static policy chose stdout-only execution, not a failure.

For VLM, render choices[0].message.content verbatim. For Cosmos output, omit the <think>...</think> block and show the answer. Do not add emojis or re-voice either backend's content.

Troubleshooting

SymptomAction
/v1/ready remains 503Treat LVS as unavailable after the warmup loop.
Readiness stdout is emptyUse the HTTP status; a 200 body may be empty.
Summary and events are emptyInspect saved summary.usage.total_chunks_processed; do not retry.
vss not foundInstall it from this skill's checkout (uv tool install <checkout>/libs/vss/cli), or report the image problem.
Run exits 4Follow stderr: configure the deployment, or configure the Markdown sink requested explicitly.
Run exits 6A post-operation memory write failed. Present the summary and separate ES/Markdown status; do not re-run.
Run exits 7Timed out. vss summarize get --job-id <id>; do not re-run.
VLM returns <think>Remove reasoning through </think> when rendering.
K8s /openapi.json looks like AgentExpected — do not use it as LVS schema.
/models 404 / HTMLProbing the bare origin — use services.lvs.url + /models or services.rt_vlm.url + /v1/models from vss configure show.

Use the debugging reference for deeper diagnostics and the deployment reference for logs or configuration. The LVS image is a multi-arch manifest, so LVS_TAG=3.3.0-rc2 is the x86/Jetson Thor default; use 3.3.0-rc2-sbsa on SBSA/DGX Spark/Grace. RT-VLM likewise needs a host-matched tag (3.3.0-26.08.2 on x86/Jetson Thor, 3.3.0-26.08.2-sbsa on SBSA/DGX Spark/Grace).

Direct API and Service Operations

For direct API questions such as models, readiness, recommended configuration, metrics, schemas, or 422 responses, use the API reference instead of the recorded-video workflow, with services.lvs.url from vss configure show as the base. /lvs is a Prefix mount, so everything LVS serves is public under it — /v1/ready, /v1/summarize, /models, /metrics — where the previous Exact-path Ingress published only readiness and summarize. A direct API question is never a substitute for a failed vss command. For deployment, restart, teardown, backend selection, or service logs, prefer vss-build-vision-ai and use the deployment reference.

Cross-reference

  • vss-build-vision-ai: deploy the lvs profile.
  • vss-manage-video-io-storage: general VIOS administration outside this ordered workflow.
  • vss-search-archive: search archived video.
  • vss-query-analytics: query stored incidents and events.
  • vss-generate-video-report and vss-ask-video: hand off here for recordings of 120 s or longer.

bump:3

© 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 14 other files (references, assets) in skills/operations/vss-summarize-video of NVIDIA-AI-Blueprints/video-search-and-summarization.

  • SKILL.md
  • assets/video-summarization.env.example
  • evals/evals.json
  • evals/lvs_api_ops.json
  • evals/lvs_profile_summarize.json
  • references/cli_usage.md
  • references/deploy-lvs-service.md
  • references/end-to-end-example.md
  • references/hitl-prompts.md
  • references/integrate-lvs-service.md
  • references/video-summarization-api.md
  • references/video-summarization-debugging.md
  • references/video-summarization-deployment.md
  • references/video-summarization-environment-variables.md
  • skill-card.md

Open the folder on GitHubat commit fdb6a7a

Compare with similar skills

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News Aggregator Skillcclank/news-aggregator-skill1.3k—~2.1kAutomated safety check: PassNone
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Questions about Vss Summarize Video

What does Vss Summarize Video do?

A skill your agent uses when summarizing a recorded video through HITL-gated LVS, falling back to vss vlm run when LVS is not ready. Vss Summarize Video is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use when summarizing a recorded video through HITL-gated LVS, falling back to vss vlm run when LVS is not ready.

When should I use Vss Summarize Video?

Vss Summarize Video fits situations like: summarizing a recorded video through HITL-gated LVS; falling back to vss vlm run when LVS is not ready.

How do I install Vss Summarize Video in Claude Code?

Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-summarize-video -a claude-code`. Or copy the skill folder (skills/operations/vss-summarize-video in NVIDIA-AI-Blueprints/video-search-and-summarization) into .claude/skills/vss-summarize-video in your project. Claude Code loads it when a task matches its description.

How do I install Vss Summarize Video in Codex?

Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-summarize-video -a codex`. Or copy the skill folder (skills/operations/vss-summarize-video in NVIDIA-AI-Blueprints/video-search-and-summarization) into .agents/skills/vss-summarize-video in your project. Codex loads it when a task matches its description.

Can I use Vss Summarize Video 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-summarize-video -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-summarize-video, .gemini/skills/vss-summarize-video, .github/skills/vss-summarize-video and .opencode/skills/vss-summarize-video in your project.

What does Vss Summarize Video need to run?

Going by SKILL.md and its folder, Vss Summarize Video needs the command-line tools its instructions call (uv, docker and kubectl). Our summary lists: Docker.

Does Vss Summarize Video access the network?

SKILL.md contains no URLs. Its commands use uv and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Vss Summarize Video safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Vss Summarize Video use?

Vss Summarize Video 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 Summarize Video use?

About 5.3k tokens (SKILL.md is roughly 21k 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 20k tokens, read only when the agent opens those files.

What are the alternatives to Vss Summarize Video?

Skills that share tags, products or a category with Vss Summarize Video: Contextpilot Savings (EfficientContext/ContextPilot, 141 stars), Context Compression (guanyang/open-agent-hub, 977 stars), News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars) and Memory Config (zilliztech/memsearch, 2.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vss Summarize Video?

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.