| 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 (see vss-deploy-profile and references/).
- NGC credentials in
$NGC_CLI_API_KEY and $NVIDIA_API_KEY for 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
/docs or /health; redeploy via vss-deploy-profile or the matching vss-deploy-* skill.
- Error: HTTP 401/403 from NGC pulls. Cause: missing/expired
NGC_CLI_API_KEY. Solution: docker login nvcr.io and 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:
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) and rtvicv-api (REST API) — every step ordering invariant, bash-batching rule, box-rendering rule, and AskQuestion contract is retained.
- For DEPLOY, the reference doc enforces its own startup contract: one-line acknowledgement → planning-tool call (
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.
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.md
under "Step N box content rule".
- After the Step 5 Results box, issue the Step 6
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.
- After the user picks a Step 6 bucket, issue the follow-up
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:
- 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" when ACTIVE > 1 (full spec: § "remove_streams
sub-flow").
- Stop the deployment → Stop app / Stop container / Full teardown.
- Check metrics & FPS → no follow-up; run
collect_metrics.sh
directly after printing the /api/v1/metrics API box.
- Check liveness / readiness → no follow-up; probe all three
health endpoints after printing their API boxes.
- 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.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):
- ❌ 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'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.
- ❌ Silently choosing
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.
- ❌ A one-line
✔ App ready in Ns, N streams, fps total Y in 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=1 and [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 +
┐, 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.
- 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 |
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