News Aggregator Skill
cclank/news-aggregator-skill
Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 44+ sources including Hacker News, Lobsters, Dev.to, GitHub, arXiv, Hugging Face Papers, AIHOT, TLDR…
Agent skill
by NVIDIA-AI-Blueprints in NVIDIA-AI-Blueprints/video-search-and-summarization
A skill your agent uses when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag…
$ npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-search-archive -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization vss-search-archive --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/operations/vss-search-archive .claude/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .claude/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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/operations/vss-search-archiveType 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-search-archive -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization vss-search-archive --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/operations/vss-search-archive .agents/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .agents/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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-search-archive -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization vss-search-archive --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/operations/vss-search-archive .cursor/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .cursor/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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/operations/vss-search-archive--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-search-archive -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA-AI-Blueprints/video-search-and-summarization vss-search-archive --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/operations/vss-search-archive .gemini/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .gemini/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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-search-archiveInstalls 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-search-archive -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/operations/vss-search-archive .github/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .github/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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-search-archive -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-search-archive --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/operations/vss-search-archive .opencode/skills/vss-search-archive && 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-search-archive" agent skill from https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop/skills/operations/vss-search-archive into .opencode/skills/vss-search-archive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vss-search-archive", 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-search-archiveA skill your agent uses when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag…
Vss Search Archive is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use this skill when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag query. Not for fresh clip Q&A, live captioning, video summarization, deployment, or source ingestion/deletion.
Its SKILL.md is about 3.3k 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 `evals/evals.json`, `evals/search.json` and `references/cli_usage.md`).
It sits in Writing & Content, covering Summarization and Deployment. 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.
Read from SKILL.md and the folder at commit fdb6a7a. 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.
Ships 1 file in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
dockeruvFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use docker and uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Vss Search Archive loads about 3.3k tokens when it runs, and up to ~7k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 1,666 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 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.
The full file from NVIDIA-AI-Blueprints/video-search-and-summarization at commit fdb6a7a, republished under its Apache-2.0 licence (© NVIDIA-AI-Blueprints). 1,666 words, ~3,344 tokens.
.claude/skills/vss-search-archive/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.Not for:
vss-ask-video.vss-summarize-video./vss-build-vision-ai.Answer from the configured deployment through the installed vss CLI. Do not
fall back to raw REST when a CLI command fails.
Hard rule — use the
vssCLI for retrieval.vss configurehas already pointed the CLI at the deployment, so every search action goes throughvss search runand nothing reaches the deployment any other way.Four things follow:
- use the
vsson PATH (see Prerequisites); neverdocker exec,kubectl exec, a pod shell, or a hand-built/api/v1/searchcall;- a named source is resolved with
vss vios listbefore search — never inferred from a display name, and never substituted when missing;- capture stdout and the exit status separately — never put the command behind
if !, which hides the real exit code, and never discard usable exit-6 results;- a failing command is a finding: report the exit code rather than routing around it, repairing the deployment, or retrying with broadened scope.
search profile with vss configure already run against its origin. Refresh configuration after first ingestion, once source provisioning has established readiness; lazy raw indexes enable frame enrichment for attribute/fusion.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.Bootstrap, exit codes, and common CLI rules live in AGENTS.md.
For an explicit request to ingest or delete a source, use
vss-manage-video-io-storage for vss vios add or vss vios delete, then
return to archive search once the source is available. The deployment's mounted
notification config owns consumer fan-out; the presence of an Agent tier does
not change the VIOS registration path. Check the requested receiver as enabled,
absent, or unknown under source management's policy contract. With an enabled
receiver, use fan-out; with confirmed absent lifecycle support, report that
limitation. Only an explicit tagging request with a confirmed absent streaming
tagging receiver and the provisioning prerequisites may use manual tagging.
With unknown policy, report unconfirmed fan-out, never unavailable indexing,
and do not start manual tagging.
If the user only asks to search a named source and it is missing, do not
ingest, switch videos, or run an unrestricted search — answer with the reply
Search workflow step 1 specifies for zero matches. If no
supported source management workflow is available, report that blocker.
1. Resolve a named source. If the request names a file, camera, or sensor,
resolve it with vss vios list (it reads the origin vss configure recorded,
so it takes no endpoint). Accept an exact name or sensor ID, or one unambiguous
normalized match; stop on zero or multiple matches. The listing is
{"count", "type", "sensors": [...]}; preserve the matched entry's
.sensors[].name and .sensors[].sensor_id, and never infer an identifier from
the display name. Below, .name and .sensor_id mean those fields.
Stopping is not the whole answer — the reply has to hand the decision back. On zero matches, the final reply states all three of:
The third is as required as the first two. Refusing to substitute is correct but incomplete: a reply that reports the mismatch and then stops leaves the user with no stated next step. Ingesting, switching to another video, or dropping the source filter and searching everything are all still forbidden (see Source management handoff). On multiple matches, name the candidates and ask which one.
2. Choose one retrieval path. Preserve the user's exact original sentence for
--original-query first — critic verification must receive that wording, while
retrieval may use the decomposed query, attributes, or object IDs. Then choose
exactly one path:
object — explicit tracked object IDs;tag — explicit lexical tag/keyword intent (BM25 over indexed VLM tags), not semantic free text;attribute — detectable properties only, no action or relation;fusion — a detectable property combined with an action or relation;embed — semantic free text.--attribute is for properties RT-CV detects on a subject (attire, PPE,
color-on-person), not object identity or an object's own color — keep red forklift wholly in --query. worker in a hard hat carrying a cone has a
property (hard hat) and an action (carrying a cone): run fusion. Reserve
embed for genuinely attribute-free intent, and tag for keyword/tag queries
that name no detectable property.
The --video-source value differs by path while the CLI's paths need different
identifiers:
| path | --video-source takes |
|---|---|
embed | preserved .sensor_id |
attribute | preserved .name |
object | preserved .name |
tag | .name (the CLI resolves it to the VST sensor ID; an already-id passes through) |
fusion | preserved .sensor_id (the tag leg accepts IDs too) |
For every path an unknown source yields an empty, narrowed result, not an error.
Before attribute or fusion, inspect vss configure show's
services.elasticsearch.indices. If no entry starts with mdx-raw-, refresh
once with vss configure --base-url using that same record's nonempty
base_url, then inspect again. Stop on a configuration failure; if the raw
family is still absent, disclose that frame enrichment is unavailable and
continue the requested retrieval. Do not construct an origin or poll indexes.
3. Invoke the CLI. Use --query for embed/fusion/tag, repeatable
--attribute for attribute/fusion, and repeatable --object-id for object.
Use --timestamp-start / --timestamp-end for time bounds. Set --source-type video_file for an uploaded recording
or rtsp for a live stream. Source type selects the fixed uploads anchor or
the live family wildcard excluding that uploads anchor, independently of source
identity and ingestion order. Carry
the requested time bounds and --top-k, and
run vss search run <path> with no endpoint, index, model, or profile flag —
vss configure owns those. Build the invocation as a Bash array and capture
stdout and the exit status separately; never clear a previously resolved source
array or hide the exit code behind if !:
: "${SEARCH_PATH:?set embed|attribute|fusion|object|tag}"
: "${SOURCE_TYPE:?set video_file or rtsp}"
: "${ORIGINAL_QUERY:?set the exact pre-decomposition user question}"
TOP_K="${TOP_K:-3}"
# Set VIDEO_SOURCES from step 1 for a named source. Explicitly set it to () only
# for a request that was unrestricted from the start; never reset a resolved scope.
declare -p VIDEO_SOURCES >/dev/null 2>&1 || { echo "Set VIDEO_SOURCES before search" >&2; exit 1; }
: "${SOURCE_SCOPED:?set true for a resolved scope; false only when unrestricted}"
if [ "${SOURCE_SCOPED}" = true ] && [ "${#VIDEO_SOURCES[@]}" -eq 0 ]; then
echo "Resolved source scope is empty; refusing an unrestricted search" >&2
exit 1
fi
SEARCH_COMMAND=(vss search run "${SEARCH_PATH}" --source-type "${SOURCE_TYPE}" \
--top-k "${TOP_K}" --original-query "${ORIGINAL_QUERY}" --raw)
for source in "${VIDEO_SOURCES[@]}"; do
SEARCH_COMMAND+=(--video-source "${source}")
done
# Append only the selected path's fields and --timestamp-start/--timestamp-end.
if SEARCH_JSON=$("${SEARCH_COMMAND[@]}"); then
STATUS=0
else
STATUS=$?
fiRead CLI usage only when tuning retrieval weights
(--fusion-method, --w-tag, etc.); do not open it for a standard search
invocation — the contract above is the whole invocation.
4. Interpret the result by exit status.
data and search_messages.search_messages): report hits only when the payload contains data, disclosing the supplied limitation. Without data, report the supplied failure; do not claim retrieval succeeded. Do not rerun or retry an individual stage.vss search run <path> --help once; correct invalid flags or values only from that help, then stop if the corrected command fails.Routes not exposed through ingress are recorded as absent by vss configure;
a search path requiring one exits 4. Report the missing capability and ask the
operator to expose its supported ingress and
refresh configuration. Never create a port-forward or use private endpoints.
After exit 5 following first ingestion, hand readiness back to source management
and refresh recorded configuration once when it is ready; never ingest implicitly.
An empty data array means zero retrieved candidates — a fact about retrieval,
not about the video; a threshold or embedding gap yields the same empty result
as a genuine absence, so do not describe what the footage contains or argue it
is not something you would expect there. If search_messages indicate degraded
retrieval, include that limitation. Do not retry, broaden scope, or use raw REST.
For each hit, read its critic_result:
confirmed: the critic found all requested visual criteria in that clip.rejected: the critic found a visual criterion was not met.unverified: the critic attempted the hit but produced no usable verdict.
This includes inaccessible media, a failed VLM call, and malformed or
inconclusive output.null: the critic did not evaluate the hit (no VLM, a critic failure,
bounds it could not check, or a hit past --critic-eval-count). Report
it as unverified.5. Report each hit honestly. For each hit, report the registered/display
source, bounded interval, retrieval score where present, the returned media
URL if present, and the exact confirmed / rejected / unverified
verdict. Retrieval score, filename, source ID, and media availability are not
visual proof. The CLI attempts critic verification by default and is fail-open:
a missing VLM or inaccessible media leaves a hit unverified and does not fail
retrieval. Do not inspect screenshots or call another verifier during this
first turn. A media URL may be empty when VST is unavailable; when present it
carries the scheme, host, and port of the origin vss configure recorded. Avoid
a mandated heading or raw JSON dump, and keep the reply implementation-neutral
— never expose a job ID, model or service name, deployment service address, CLI
flag, or a raw sensor_id; say "visual verification" and report its
verdict. The one exception: when the user asks which commands to run, show the
commands.
A media URL is returned data, not a service address: reproduce it whole,
including the source ID in its path; the sensor_id rule is about how you name
the source in prose. Report each hit's verdict, and its critic_result.criteria_met
when nonempty, without reading a verdict into the criteria.
6. Offer a Verification Step only when the whole set is unverified. If and
only if every displayed result in the nonempty set is unverified, offer a
Verification Step by asking whether the user wants the hits checked through
vss-ask-video. If any hit is
confirmed or rejected, offer nothing and hand off nothing. On explicit
confirmation, load search-result verification
and hand off only the displayed, bounded hits, preserving the original question;
do not rerun search. Never hand off a partially verified result set.
© 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
SKILL.md and 6 other files (scripts, references) in skills/operations/vss-search-archive of NVIDIA-AI-Blueprints/video-search-and-summarization.
Open the folder on GitHubat commit fdb6a7a
Vss Search Archive 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 Search Archive this skillNVIDIA-AI-Blueprints/video-search-and-summarization | 1.9k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| News Aggregator Skillcclank/news-aggregator-skill | 1.3k | — | ~2.1k | Automated safety check: Pass | None | |
| Deepgram JS Text Intelligencedeepgram/deepgram-js-sdk | 276 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Key Takeawaysaipoch/medical-research-skills | 1.9k | — | ~2.3k | Automated safety check: Pass | MIT | |
| Vss Search ArchiveNVIDIA/skills | 3.6k | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Summarize Anythingswyxio/skills | 176 | — | ~6.3k | Automated safety check: Pass | MIT |
cclank/news-aggregator-skill
Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 44+ sources including Hacker News, Lobsters, Dev.to, GitHub, arXiv, Hugging Face Papers, AIHOT, TLDR…
deepgram/deepgram-js-sdk
A skill your agent uses when writing or reviewing JavaScript/TypeScript in this repo that calls Deepgram Text Intelligence / Read (/v1/read) for sentiment, summarization, topic detection, and intent…
aipoch/medical-research-skills
Extracts and summarizes key takeaways from documents, meeting notes, articles, and other text content.
NVIDIA/skills
A skill your agent uses to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search.
swyxio/skills
Summarizes arbitrarily long text (1k-1M words) using recursive map-reduce with any LLM backend.
A skill your agent uses when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS…
NVIDIA-AI-Blueprints/video-search-and-summarization
Measure retrieval quality and latency of a deployed VSS search profile by ingesting a labelled dataset and running the vss CLI across retrieval paths.
NVIDIA-AI-Blueprints/video-search-and-summarization
Plan, run, and diagnose reproducible RT-VLM GPU performance canaries and benchmarks.
NVIDIA-AI-Blueprints/video-search-and-summarization
Add agent-ready vision capabilities — dense captioning, detection, search, alerting, summarization — to an agent or application through a customizable, self-contained vision stack built on the…
NVIDIA-AI-Blueprints/video-search-and-summarization
Measure whether an RT-VLM configuration change altered caption quality — capture paired baseline and candidate captions for a set of videos, score both against a ground truth with an LLM judge, and…
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…
NVIDIA-AI-Blueprints/video-search-and-summarization
A skill your agent uses when operating VSS alert workflows — real-time monitoring, Alert-Bridge subscriptions, verification verdicts, on-demand verification, always-on operation, Slack…
A skill your agent uses when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag…. Vss Search Archive is an agent skill from NVIDIA-AI-Blueprints/video-search-and-summarization. Use this skill when a user wants to search archived VSS video that is already registered in a configured deployment — by natural-language, similarity, attribute, object-ID, or lexical tag query.
Vss Search Archive fits situations like: A user wants to search archived VSS video that is already registered in a configured deployment — by natural-language; lexical tag query.
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-search-archive -a claude-code`. Or copy the skill folder (skills/operations/vss-search-archive in NVIDIA-AI-Blueprints/video-search-and-summarization) into .claude/skills/vss-search-archive in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-search-archive -a codex`. Or copy the skill folder (skills/operations/vss-search-archive in NVIDIA-AI-Blueprints/video-search-and-summarization) into .agents/skills/vss-search-archive 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-AI-Blueprints/video-search-and-summarization --skill vss-search-archive -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-search-archive, .gemini/skills/vss-search-archive, .github/skills/vss-search-archive and .opencode/skills/vss-search-archive in your project.
Going by SKILL.md and its folder, Vss Search Archive needs a shell for the scripts in its folder and the command-line tools its instructions call (docker and uv). Our summary lists: A Bash shell; Docker.
SKILL.md contains no URLs. Its commands use docker and uv, 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 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.
Vss Search Archive 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 3.3k tokens (SKILL.md is roughly 13k 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 3.6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Vss Search Archive: News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars), Deepgram JS Text Intelligence (deepgram/deepgram-js-sdk, 276 stars), Key Takeaways (aipoch/medical-research-skills, 1.9k stars) and Vss Search Archive (NVIDIA/skills, 3.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.