Use this skill when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice. Trigger when the user says things like 'deploy rtvi-cv', 'start warehouse 2d', 'add a stream', 'check rtvi-cv health', or 'stop the perception container'. Not for VLM, embedding, or analytics — use the matching vss-* skill.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionvss-deploy-detection-tracking-2dExecute the skills CLI command in your project's root directory to begin installation:
Fetches vss-deploy-detection-tracking-2d from nvidia/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate vss-deploy-detection-tracking-2d. Access via /vss-deploy-detection-tracking-2d in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| name | vss-deploy-detection-tracking-2d |
| description | "Use this skill when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice. Trigger when the user says things like 'deploy rtvi-cv', 'start warehouse 2d', 'add a stream', 'check rtvi-cv health', or 'stop the perception container'. Not for VLM, embedding, or analytics — use the matching vss-* skill." |
| license | Apache-2.0 |
| metadata | version: "3.2.0" github-url: "https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization" tags: "nvidia rtvi-cv deployment rest-api docker deepstream ngc warehouse smartcity sparse4d gdino rt-detr metropolis stream-management health-check metrics" |
Deploy, debug, and operate the RTVI-CV detection / tracking 2D microservice and drive its REST API.
$HOST_IP (see vss-deploy-profile and references/).$NGC_CLI_API_KEY and $NVIDIA_API_KEY for any image pulls.curl, jq, and Docker available on the caller.Follow the routing tables and step-by-step workflows below. Each section that ends in workflow, quick start, or flow is intended to be executed top-to-bottom. Detailed reference material lives in references/ and helper scripts live in scripts/ — call them via run_script when the skill points to a script by name.
Worked end-to-end examples are kept under evals/ (each *.json manifest contains a runnable scenario) and inline in the per-workflow curl blocks below. Run a Tier-3 evaluation with nv-base validate <this-skill-dir> --agent-eval to replay them.
/docs or /health; redeploy via vss-deploy-profile or the matching vss-deploy-* skill.NGC_CLI_API_KEY. Solution: docker login nvcr.io and re-export the key before retrying.docker compose down.Unified skill for the Real Time Video Intelligence CV (RTVI-CV) microservice. Two action surfaces in one skill:
references/deploy-vss-detection-tracking-2d.mdreferences/usage-vss-detection-tracking-2d.mdService:
rtvi-cv(metropolis_perception_app) Image:nvcr.io/<org>/<repo>:<tag>— user-supplied at deploy time REST port:9000(/api/v1—/live,/ready,/startup,/metrics,/stream/add,/stream/remove, embeddings) Hardware: x86/aarch64 dGPU (T4, A100, L40, H100, B200, RTX), SBSA (Spark, Grace-Hopper), Jetson (Thor, Orin, Xavier)
| User intent (sample phrasing) | Flow | Load this reference |
|---|---|---|
deploy rtvi-cv warehouse 2d, run rtvicv warehouse-3d with 4 streams, start smartcity gdino, launch perception app, bring up sparse4d | DEPLOY | references/deploy-vss-detection-tracking-2d.md |
stop rtvi-cv, tear down, kill the perception container, cleanup rtvicv-perception-docker | TEARDOWN (handled by deploy doc → "Mode Selection") | references/deploy-vss-detection-tracking-2d.md + references/teardown-flow.md |
check rtvi-cv logs, diagnose rtvi-cv crashing, troubleshoot healthcheck failing, rtvi-cv won't start | DEBUG | references/deploy-vss-detection-tracking-2d.md + references/troubleshooting.md |
add a stream, remove camera, list streams, health check, is rtvi-cv ready, get metrics, what's the FPS, check GPU usage, generate text embeddings, call rtvi-cv api | API USAGE | references/usage-vss-detection-tracking-2d.md + references/api-reference.md |
Selection rule: match the user's phrasing against the table above and immediately load the corresponding reference file. Do not mix the flows — DEPLOY assumes no running container yet; API USAGE assumes the container is already running on http://<host>:9000.
If intent is genuinely ambiguous (e.g., the user says just "I want to use rtvi-cv"), ask one AskQuestion: deploy a new instance, or call an already-running one?
vss-deploy-detection-tracking-2d/
├── SKILL.md # this file (routing + contracts)
├── assets/ # data files (deploy-defaults.yml — single source of truth for tags / refs / paths / GPU)
├── evals/ # Tier-3 eval manifests (deploy-evals.json, usage-evals.json)
├── scripts/ # 23 bash + python helpers (see `scripts/` for the full inventory)
└── references/ # workflow runbooks (deploy / api-usage / teardown / troubleshooting / …)
For the full per-file inventory and what each reference covers, see
references/workflow-reference.md.
All scripts are invoked from the skill root via $SKILL_DIR/scripts/<name> — paths inside the deploy reference doc are preserved verbatim and resolve correctly when the agent runs from skill root.
Helpers live in scripts/ and are invoked from the skill root by name —
call each via run_script("scripts/<name>") so the agent records a
proper tool invocation.
| Script | Purpose | Arguments |
|---|---|---|
load_defaults.sh | Detect platform (x86 dGPU / SBSA / Jetson) and resolve YAML defaults from assets/deploy-defaults.yml. | --usecase <name> |
fetch_resources.sh | Download + extract NGC resources, scan for layout. | --ngc-ref <ref> (optional) |
apply_in_container.sh | Host-side wrapper for Step 4 (apply_config.sh inside the running container). | <container_name> |
apply_config.sh | In-container path-substitution, batch, sink, sources, engine cache. | <usecase> <stream_count> <sink_type> |
start_app_in_container.sh | Host-side wrapper for Step 5 (run_app_and_wait.sh). | <container_name> |
run_app_and_wait.sh | In-container app launch + readiness + metrics + log. | <config_path> |
add_streams.sh / update_stream_sources.sh | REST stream lifecycle for Step 6. | <rtsp_or_file_uri>... |
collect_metrics.sh | Pull /api/v1/metrics snapshot. | none |
discover_streams.sh | Enumerate active streams via /stream/get-stream-info. | none |
synthesize_docker_run.sh | Print the platform-correct docker run line for the resolved env. | none |
render_box.sh | Render the fixed-width step receipt. | <step_label> |
calibration_manager.py | Manage calibration artefacts + per-use-case engine cache invalidation. | --usecase <name> --reset |
For the full inventory of helpers (cache, GPU checks, setup) browse
scripts/; each script's --help describes its arguments.
vss-deploy-detection-tracking-2d (deploy/teardown/debug) and rtvicv-api (REST API) — every step ordering invariant, bash-batching rule, box-rendering rule, and AskQuestion contract is retained.TodoWrite array of 5 todos, OR 5 successive TaskCreate calls on newer Claude Code) → Step 1 question. Do not narrate, do not pre-flight, and never print "loading TodoWrite/TaskCreate" or any deferred-tool resolution prose — the planning tool is loaded silently.When running the DEPLOY / TEARDOWN / DEBUG flow, the agent MUST honour all four items below on every successful deploy. These are the user's only feedback channel between steps; skipping any of them is a behaviour regression.
references/deploy-vss-detection-tracking-2d.md
under "Step N box content rule".AskUserQuestion
from references/next-steps.md § "11.c"
— never replace it with a free-form Next steps bullet list. The
menu is the deploy's exit handle: it lets the user run metrics,
manage streams, tail logs, or tear down with one click instead of
having to remember curl URLs.AskUserQuestion from references/next-steps.md
§ "11.d" — never substitute prose + ready-to-copy curl examples + a
free-text "want me to run X?" question. Each bucket has its own
menu of concrete actions; the user picks the action, then the skill
emits the API box and runs the curl. Per-bucket follow-ups:
/stream/get-stream-info — one option
per active stream labelled <camera_id> · <camera_url> plus
"Remove ALL" when ACTIVE > 1 (full spec: § "remove_streams
sub-flow").collect_metrics.sh
directly after printing the /api/v1/metrics API box.references/deploy-vss-detection-tracking-2d.md
under "Step N box content rule". Step 4 (Apply configuration) is
where the agent collapses most often — its canonical
per-use-case key list lives in
references/apply-config.md
§ "Per-use-case complete edit list", and the agent MUST emit one
✔ [section] key=value — annotation row per key in that table for
the active use case + settings. A section with 5 keys → 5 rows; a
section with 6 keys → 6 rows. Never one overview row per section.Forbidden (these are the shortcuts the agent falls back to under pressure, and they break the user's UX):
✔ <pinned-values> summary line followed by the widget — never any
scaffolding around tool resolution.TaskCreate's
description field. When TaskCreate is the available planning
tool, issue 5 separate TaskCreate calls back-to-back (one per
step). See references/task-list.md § "Initial TaskCreate calls"
for the verbatim template. Same rule for TodoWrite — one call with
all 5 todos in the todos:[…] array; never one todo whose content
is a multi-line list.dynamic stream-mode. The skill default is
stream_mode=static — the agent bakes auto-discovered file:// URLs
into the DS main config's [source-list] block before app start.
Switch to dynamic only when the user explicitly asks ("add streams
later via REST", "use dynamic stream mode") OR when they pick dynamic
in the Step 2 AskQuestion. Picking dynamic for a generic "deploy
rtvi-cv with N streams" query breaks the deploy rubric and the
user's /metrics expectations. See
references/pipeline-config.md
§ "Defaults — the skill is static-mode by default" for the full
rationale.✔ App ready in Ns, N streams, fps total Y in place of
the Step 5 Results box.+, -, =, *) instead of light
box-drawing chars (┌ ─ ┐ │ └ ┘).✔ Batch size 3 (tile grid: 1×3) → required: 5 separate rows
([streammux] batch-size=3, [primary-gie] batch-size=3,
[source-list] max-batch-size=3, [tiled-display] rows=1,
[tiled-display] columns=3).✔ Output sink eglsink → required: one row per sink key
(4 keys for eglsink, e.g. [sink0] enable=1, type=2,
sync=0, qos=0 — read apply-config.md for the exact list).✔ Sources static (3 streams, http-port=9000) → required: six
annotated [source-list] rows.✔ Tile grid 1 row × 3 cols (single row) → required: two
rows, [tiled-display] rows=1 and [tiled-display] columns=3.The geometry contract for every step-exit box (Step 1 through Step 5 Results). The same shape across every box; only the title and the body rows change per step.
┌ at column 1, ┐ at
column 128. Wider terminals leave the box flush-left; do not stretch
it. Inner content area is 124 chars (with one space margin on
each side inside the │ borders).┌ ─ ┐ │ └ ┘. No +, -, =,
* ASCII fallbacks.┌ + N₁ dashes + ␣ + title + ␣
┐, where N₁ + N₂ + len(title) + 2 = 126. Distribute
the pad: N₁ = floor((126 − len(title) − 2) / 2),
N₂ = 126 − len(title) − 2 − N₁. N₁ and N₂ differ by at most 1.│ <content padded to inner-content 124> │ per fact.
Each fact line uses the ✔ <key-padded-to-13> <value> form (two
spaces in, glyph, key right-padded to 13, two spaces, value).│ <124 spaces> │ between
logical groups (e.g. Identity / Model / Videos in Step 1) so the
user can scan the box at a glance.└ + 126 dashes + ┘ — solid border, no title.Standard step titles (used at the top of each step's box):
┌─────────────────────────────────────────────────────── Deploy targets ───────────────────────────────────────────────────────┐
┌─────────────────────────────────────────────────── Pipeline configuration ───────────────────────────────────────────────────┐
┌───────────────────────────────────────────────────────── Container ──────────────────────────────────────────────────────────┐
┌──────────────────────────────────────────────────── Apply configuration ─────────────────────────────────────────────────────┐
┌──────────────────────────────────────────────── Perception Application — Plan ───────────────────────────────────────────────┐
┌────────────────────────────────────────────── Perception Application — Results ──────────────────────────────────────────────┐
Per-step content rules (which rows go in which box, mode-aware row
hiding, the apply-config sectioned layout, the Step 5 PLAN-then-RESULT
pattern, the Step 3 docker run synthesis requirement) live in
references/deploy-vss-detection-tracking-2d.md
under "Step N box content rule" — read those when rendering the
corresponding step.
| Phrase | Flow |
|---|---|
deploy rtvicv warehouse 2d with 4 streams and display | DEPLOY |
run smartcity gdino on gpu 1 | DEPLOY |
stop the perception container | TEARDOWN (deploy doc) |
rtvi-cv healthcheck failing | DEBUG (deploy doc + troubleshooting) |
add a stream to rtvi-cv | API USAGE |
is rtvi-cv ready on localhost:9000 | API USAGE |
get rtvi-cv metrics | API USAGE |
generate text embeddings via rtvi-cv | API USAGE |
bump:1
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend vss-deploy-detection-tracking-2d for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: vss-deploy-detection-tracking-2d is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
vss-deploy-detection-tracking-2d fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
vss-deploy-detection-tracking-2d is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
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