vss-deploy-detection-tracking-2d
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.
Works with
0
total installs
0
this week
1.7K
GitHub stars
0
upvotes
Install Skill
Run in your terminal
0
installs
0
this week
1.7K
stars
Installation Guide
How to use vss-deploy-detection-tracking-2d on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
vss-deploy-detection-tracking-2d
Run the install command
Execute 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.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
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.
Security Notice
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.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
| 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" |
Purpose
Deploy, debug, and operate the RTVI-CV detection / tracking 2D microservice and drive its REST API.
Prerequisites
- Active VSS deployment reachable on
$HOST_IP(seevss-deploy-profileandreferences/). - NGC credentials in
$NGC_CLI_API_KEYand$NVIDIA_API_KEYfor any image pulls. curl,jq, and Docker available on the caller.
Instructions
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.
Examples
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.
Limitations
- Requires the matching VSS profile / microservice to be deployed and reachable from the caller.
- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.
- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.
Troubleshooting
- Error: REST call returns connection refused. Cause: target microservice not running. Solution: probe
/docsor/health; redeploy viavss-deploy-profileor the matchingvss-deploy-*skill. - Error: HTTP 401/403 from NGC pulls. Cause: missing/expired
NGC_CLI_API_KEY. Solution:docker login nvcr.ioand re-export the key before retrying. - Error: container OOM or model fails to load. Cause: insufficient GPU memory for the selected profile. Solution: switch to a smaller variant or free GPUs via
docker compose down.
RTVI-CV — Detection & Tracking (Unified Skill)
Unified skill for the Real Time Video Intelligence CV (RTVI-CV) microservice. Two action surfaces in one skill:
- Deploy / operate / debug / tear down the RTVI-CV container locally → see
references/deploy-vss-detection-tracking-2d.md - Call the RTVI-CV REST API (streams, health, metrics, embeddings) on a running instance → see
references/usage-vss-detection-tracking-2d.md
Service:
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)
Action routing — pick once per invocation
| 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?
What lives where
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.
Available Scripts
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.
How to use this skill
- Read this file first. It only routes — it does not contain workflows.
- Match the user's intent against the routing table above.
- Load exactly one reference doc (DEPLOY or API USAGE). Don't preload both — each reference is large and contains its own full contract.
- Follow the loaded reference exactly. The reference docs are the byte-for-byte preserved contracts from the predecessor skills
vss-deploy-detection-tracking-2d(deploy/teardown/debug) andrtvicv-api(REST API) — every step ordering invariant, bash-batching rule, box-rendering rule, andAskQuestioncontract is retained. - For DEPLOY, the reference doc enforces its own startup contract: one-line acknowledgement → planning-tool call (
TodoWritearray of 5 todos, OR 5 successiveTaskCreatecalls 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.
Output contract — DEPLOY flow
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.
- Render every step's exit in a fixed-width box — Step 1 Deploy
targets, Step 2 Pipeline configuration, Step 3 Container, Step 4
Apply configuration, Step 5 Plan + Results. Not just the final
summary. The box is the user's step receipt. Geometry is fixed (see
§ "Universal box format" below). Per-step content rules (what
rows go inside each box) live in
references/deploy-vss-detection-tracking-2d.mdunder "Step N box content rule". - After the Step 5 Results box, issue the Step 6
AskUserQuestionfromreferences/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. - After the user picks a Step 6 bucket, issue the follow-up
AskUserQuestionfromreferences/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:- Manage streams → Add / Remove / List. Remove builds its
options dynamically from
/stream/get-stream-info— one option per active stream labelled<camera_id> · <camera_url>plus "Remove ALL" whenACTIVE > 1(full spec: § "remove_streamssub-flow"). - Stop the deployment → Stop app / Stop container / Full teardown.
- Check metrics & FPS → no follow-up; run
collect_metrics.shdirectly after printing the/api/v1/metricsAPI box. - Check liveness / readiness → no follow-up; probe all three health endpoints after printing their API boxes.
- Manage streams → Add / Remove / List. Remove builds its
options dynamically from
- Render the FULL per-step content, not an overview row —
rendering the box is necessary but not sufficient. Each step has a
row composition spec in
references/deploy-vss-detection-tracking-2d.mdunder "Step N box content rule". Step 4 (Apply configuration) is where the agent collapses most often — its canonical per-use-case key list lives inreferences/apply-config.md§ "Per-use-case complete edit list", and the agent MUST emit one✔ [section] key=value — annotationrow 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):
- ❌ Internal tool-loading narration. Never print "I need to load
TodoWrite (a deferred tool the skill calls for the task widget)",
"Loading TaskCreate…", "Calling ToolSearch for the planning tool…",
or any other text about resolving / loading / fetching deferred tools.
The agent loads tools silently. The user only ever sees the
✔ <pinned-values>summary line followed by the widget — never any scaffolding around tool resolution. - ❌ Collapsing all 5 deploy steps into a single
TaskCreate'sdescriptionfield. WhenTaskCreateis the available planning tool, issue 5 separateTaskCreatecalls back-to-back (one per step). Seereferences/task-list.md§ "InitialTaskCreatecalls" for the verbatim template. Same rule forTodoWrite— one call with all 5 todos in thetodos:[…]array; never one todo whosecontentis a multi-line list. - ❌ Silently choosing
dynamicstream-mode. The skill default isstream_mode=static— the agent bakes auto-discoveredfile://URLs into the DS main config's[source-list]block before app start. Switch todynamiconly when the user explicitly asks ("add streams later via REST", "use dynamic stream mode") OR when they pickdynamicin the Step 2 AskQuestion. Pickingdynamicfor a generic "deploy rtvi-cv with N streams" query breaks the deploy rubric and the user's/metricsexpectations. Seereferences/pipeline-config.md§ "Defaults — the skill is static-mode by default" for the full rationale. - ❌ A one-line
✔ App ready in Ns, N streams, fps total Yin place of the Step 5 Results box. - ❌ ASCII box-drawing chars (
+,-,=,*) instead of light box-drawing chars (┌ ─ ┐ │ └ ┘). - ❌ Skipping Step 6 on the assumption "the user knows what to do next".
- ❌ After Step 6, dumping a markdown wall of prose + multiple curl blocks + a closing "want me to run any of these?" — that's the shape the agent falls back to and it bypasses both the 11.d menu and the per-API-call box. The user picks from a menu; the skill shows the resolved API box; the skill runs it. No free-text Q.
- ❌ Step 4 overview collapses — these are explicitly banned by the
deploy doc's Step 4 content rule:
✔ 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=1and[tiled-display] columns=3.
Universal box format
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.
- Width: 128 chars corner-to-corner —
┌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). - Light box-drawing chars only:
┌ ─ ┐ │ └ ┘. No+,-,=,*ASCII fallbacks. - Top border — title CENTERED:
┌+ N₁ dashes +␣+ title +␣- N₂ dashes +
┐, whereN₁ + 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.
- N₂ dashes +
- Body: one
│ <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). - Blank lines between groups: render
│ <124 spaces> │between logical groups (e.g. Identity / Model / Videos in Step 1) so the user can scan the box at a glance. - Bottom border:
└+ 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.
Quick triggers (mnemonic)
| 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
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ 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.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Related Skills
dynamo-router-starter
0nvidia/skills
cuopt-install
0nvidia/skills
holoscan-install-container
0nvidia/skills
jetson-print-bsp-info
0nvidia/skills
jetson-memory-audit
0nvidia/skills
jetson-speculative-decoding
0nvidia/skills
Reviews
- AAisha Anderson★★★★★Dec 24, 2024
I recommend vss-deploy-detection-tracking-2d for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- PPratham Ware★★★★★Dec 16, 2024
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
- MMin Bansal★★★★★Dec 16, 2024
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMeera Li★★★★★Dec 16, 2024
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.
- AAisha Abbas★★★★★Dec 12, 2024
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
- HHarper Taylor★★★★★Nov 15, 2024
vss-deploy-detection-tracking-2d fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Nov 7, 2024
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMin Menon★★★★★Nov 7, 2024
Registry listing for vss-deploy-detection-tracking-2d matched our evaluation — installs cleanly and behaves as described in the markdown.
- CCharlotte Perez★★★★★Nov 7, 2024
vss-deploy-detection-tracking-2d is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNoor Patel★★★★★Nov 3, 2024
vss-deploy-detection-tracking-2d reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 72
Discussion
Comments — not star reviews- No comments yet — start the thread.