Official agent skill

Amc Run Rtsp Calibration

by NVIDIA in NVIDIA/skills

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API.

OfficialApache-2.0Auto-check: notesBackend & APIs

Install Amc Run Rtsp Calibration

skills CLI
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --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/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .claude/skills/amc-run-rtsp-calibration && 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
amc-run-rtsp-calibration
GitHub stars
3.6k
Token cost
~4.6k tokens
SKILL.md length
1,591 words
Files
6 (incl. scripts)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API.

  • Works in 7 steps: Verify AMC and VIOS → Create Project → Start RTSP Capture → …
  • The user provides RTSP URLs
  • SKILL.md covers When to Use This Skill, Prerequisites, Data Privacy and What to Ask the User, plus 6 more sections
  • Runs Python scripts from its folder; calls docker, curl and python3

What it does

Amc Run Rtsp Calibration is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts (for example `BENCHMARK.md`, `evals/evals.json` and `scripts/run_rtsp_calibration.py`).

It sits in Backend & APIs, covering Performance reviews and REST APIs. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • The user provides RTSP URLs
  • Asks to calibrate live cameras
  • VIOS records clips
  • AMC ingests them

Example prompts

  • “/amc-run-rtsp-calibration”

Requirements

  • Python 3
  • Docker

Workflow steps

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

  1. Verify AMC and VIOS
  2. Create Project
  3. Start RTSP Capture
  4. Poll Capture, Then Ingest
  5. Upload Settings, Alignment, Layout, and Optional Files
  6. Verify, Calibrate, Poll, and Fetch Results
  7. Optional VGGT Refinement

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • curl
    • python3

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

  • Network

    No URLs in SKILL.md. Its commands use docker and curl, 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

Amc Run Rtsp Calibration loads about 4.6k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 1,591 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:230
    ng the check above. Do not read `compose/.env` for project paths during this workflow.

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.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,591 words, ~4,558 tokens.

Download SKILL.mdSave it as .claude/skills/amc-run-rtsp-calibration/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
amc-run-rtsp-calibration
description
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
owner
NVIDIA CORPORATION
service
auto-magic-calib
version
1.0.0
reviewed
2026-06-15
license
Apache-2.0
permissions
env, file_read, network
metadata.author
Shubham Agrawal <shuagrawal@nvidia.com>
metadata.tags
amc, calibration, rtsp, vios, rest-api, camera, python

Skill: Calibrate from RTSP Streams

When to Use This Skill

Activate this skill when the user wants to calibrate from live RTSP camera streams. Typical prompts:

  • "calibrate RTSP streams" / "calibrate from live cameras"
  • "run AMC on RTSP"
  • The user provides one or more rtsp://... URLs

VIOS records fixed-duration clips from each stream, the AMC microservice ingests those clips into a project, then the workflow follows the same verification, calibration, polling, and results path as pre-recorded MP4 calibration.

Do not use this skill for local MP4 files already on disk; route those requests to skills/amc-run-video-calibration/SKILL.md. Do not use it for the bundled sample dataset; route that to skills/amc-run-sample-calibration/SKILL.md.

Never reuse files from the bundled sample dataset, extracted sample zip, assets/, or previous projects for RTSP calibration unless the user explicitly provides those paths for this RTSP scene. Similar camera names, stream counts, or cam_00/cam_01 ordering are not evidence that sample alignment, layout, GT, or detector settings apply.

Prerequisites

  • AMC microservice and UI running (follow skills/amc-setup-calibration-stack/SKILL.md if needed).
  • VIOS is running and reachable from the AMC microservice.
  • VIOS_BASE_URL is configured in the AMC microservice environment before capture starts.
  • RTSP URLs are reachable from the VIOS host.
  • Camera streams have enough moving people/objects for calibration; record at least 2-3 minutes when possible.
  • Python 3 with requests installed when using the bundled script.

Data Privacy

RTSP URLs may contain usernames, passwords, hostnames, or network topology. Do not print full RTSP URLs if credentials are embedded. This skill does not handle bearer credentials; if the VIOS deployment requires authentication, stop and hand the user to a manual admin-managed workflow instead of collecting or relaying secrets in chat, scripts, or logs.

What to Ask the User

Required
  1. RTSP URLs, one per camera.
  2. Camera names, one per stream. Use cam_00, cam_01, ... if the user does not provide names.
  3. Recording duration in seconds. Minimum is 60; prefer 120-180 or more when the scene has sparse motion.
  4. Microservice URL, for example http://<HOST_IP>:8000 or http://<HOST_IP>:8000/v1.
  5. Project name.
  6. Calibration asset source for this RTSP scene:
    • a local directory to scan, such as /data/my_rtsp_calib/;
    • explicit paths to settings, alignment, layout, and optional GT files; or
    • confirmation that the user will upload/tune settings and alignment in the AMC UI.

If the user does not provide a local asset source, stop and ask whether they want to provide a path or use UI upload. Give the UI link as http://<HOST_IP>:<AUTO_MAGIC_CALIB_UI_PORT>; the default UI port is 5000.

Auto-Detected or Asked

RTSP clips are recorded by VIOS, so there is no local videos directory to anchor file discovery. Only scan a directory the user explicitly provided for this RTSP scene. If the user provides a settings file path, use that file's directory as the scan directory. If the user provides a calibration asset directory, scan only that directory. Otherwise ask this question before planning uploads or calibration:

Do you have a local calibration asset directory or settings file for these RTSP streams, or should you upload/tune settings and alignment in the AMC UI at http://<HOST_IP>:<AUTO_MAGIC_CALIB_UI_PORT>?

FileCandidate filenamesUI fallback
Calibration settingsExplicit user path, or settings.json, config.json, or calibration_config.json in the user-provided asset directoryUI Step 3: Parameters
Alignment JSONExplicit user path, or alignment_data.json in the user-provided asset directory/settings directoryUI Step 4: Alignment
Layout PNGExplicit user path, or layout.png in the user-provided asset directory/settings directoryUI Step 4: Alignment
Ground truth zipOptional explicit user path, or GT.zip/gt.zip in the user-provided asset directoryOmit metrics

Posting the settings file replaces UI Step 3 and may pin detector or detector_type. If it pins resnet or transformer, pass that same detector to /calibrate. If no settings file pins a detector, ask the user which detector to use; do not silently default to resnet.

Optional
  1. sensor_id per stream if the cameras are already registered in VIOS. Leave unset for auto-registration.
  2. Ground truth zip (GT.zip) for evaluation metrics.
  3. Focal lengths, one per camera.
  4. Whether to run VGGT refinement after AMC completes, only when the project reports vggt_state == "READY".

Instructions

The bundled script in scripts/run_rtsp_calibration.py implements this sequence end to end. Use the prose below for decisions, UI fallback, and troubleshooting.

Step 0 - Verify AMC and VIOS

Confirm the AMC microservice is reachable:

bash
curl -sf http://<HOST_IP>:<MS_PORT>/v1/ready

Confirm VIOS is reachable before starting capture. Probe in this order and stop at the first working URL:

bash
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }
VIOS_BASE_URL=""

# Default local VIOS port.
if curl -sf http://localhost:30888/vst/api/v1/sensor/list >/dev/null 2>&1; then
  VIOS_BASE_URL="http://localhost:30888"
  echo "VIOS detected at $VIOS_BASE_URL"
fi

# Running AMC microservice container environment.
if [ -z "$VIOS_BASE_URL" ]; then
  VIOS_BASE_URL=$(docker exec auto-magic-calib-ms-1 printenv VIOS_BASE_URL 2>/dev/null)
fi

if [ -n "$VIOS_BASE_URL" ]; then
  curl -sf "${VIOS_BASE_URL}/vst/api/v1/sensor/list" >/dev/null \
    && echo "VIOS up at $VIOS_BASE_URL" \
    || { echo "VIOS_BASE_URL=$VIOS_BASE_URL is set but not responding"; VIOS_BASE_URL=""; }
fi

[ -n "$VIOS_BASE_URL" ] || {
  echo "VIOS is not reachable. Export VIOS_BASE_URL=http://<VIOS_HOST>:30888 and relaunch the AMC microservice." >&2
  exit 1
}

If VIOS is not reachable, ask the user to deploy VIOS and provide the base URL. Do not start RTSP capture until ${VIOS_BASE_URL}/vst/api/v1/sensor/list returns 200.

If VIOS is reachable but the AMC microservice is missing VIOS_BASE_URL, do not edit checked-in compose files. Export the variable and relaunch the microservice with a temporary compose override:

bash
cd "$REPO_ROOT/compose"
export VIOS_BASE_URL="http://<VIOS_HOST>:30888"
OVERRIDE_FILE="${TMPDIR:-/tmp}/amc-vios.override.yml"
cat > "$OVERRIDE_FILE" <<'YAML'
services:
  auto-magic-calib-ms:
    environment:
      - VIOS_BASE_URL=${VIOS_BASE_URL}
YAML

docker compose -f compose.yml -f "$OVERRIDE_FILE" up -d auto-magic-calib-ms
docker exec auto-magic-calib-ms-1 printenv VIOS_BASE_URL

A host-shell export alone is not enough after the container is already running; the microservice process must be restarted with VIOS_BASE_URL in its environment.

Step 1 - Create Project

POST /v1/create_project with form field project_name. Save the returned project_id.

Step 2 - Start RTSP Capture
POST /v1/rtsp/capture/<project_id>
Content-Type: application/json

{
  "streams": [
    {"rtsp_url": "rtsp://...", "camera_name": "cam_00", "sensor_id": null},
    {"rtsp_url": "rtsp://...", "camera_name": "cam_01", "sensor_id": null}
  ],
  "duration_seconds": 180,
  "ssl_verify": true
}

The response can nest session fields under session:

{"code": 0, "message": "...", "session": {"session_id": "...", "status": "STARTING"}}

Save session.session_id.

Step 3 - Poll Capture, Then Ingest

Poll every 10 seconds:

GET /v1/rtsp/capture/<project_id>/<session_id>

Session lifecycle:

STARTING -> RECORDING -> COMPLETED -> INGESTING -> INGESTED
                       -> ERROR
RECORDING -> CANCELLED

When capture reaches COMPLETED, ingest the recorded clips into the AMC project:

POST /v1/rtsp/capture/<project_id>/<session_id>/ingest

After ingest succeeds, the project has video files attached and the rest of the workflow matches the MP4 upload path.

Need to stop early: POST /v1/rtsp/capture/<project_id>/<session_id>/stop. A partial clip can still be ingested if VIOS produced one.

Other session endpoints:

  • GET /v1/rtsp/sessions/<project_id> - list sessions for a project.
Show full SKILL.md (691 more words)Show less
Step 4 - Upload Settings, Alignment, Layout, and Optional Files

Resolve local files using the anchor-file pattern above. Upload resolved files:

FileEndpointNotes
Calibration settingsPOST /v1/config/<project_id>JSON body posted as-is; replaces UI Step 3
Alignment JSONPOST /v1/upload_alignment/<project_id>Multipart alignment_file
Layout PNGPOST /v1/upload_layout/<project_id>Multipart layout_file
Ground truth zipPOST /v1/upload_gt_file/<project_id>Optional
Focal lengthsPOST /v1/upload_focal_length/<project_id>Optional repeated focal_length values

Use only files from explicit user-provided paths or a user-provided calibration asset directory. Do not extract or scan sample data to find fallback settings, alignment, layout, or GT.

If settings are missing, direct the user to UI Step 3: Parameters at http://<HOST_IP>:<AUTO_MAGIC_CALIB_UI_PORT>, then ask which detector to use (resnet or transformer) before calibration. If alignment or layout is missing, direct the user to UI Step 4: Alignment for this project. For RTSP projects, videos are already ingested; do not re-upload videos in the UI fallback.

Before continuing after UI Step 4, verify:

bash
PROJECT_ID=<project_id>
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }
HOST_PROJECTS="${PROJECTS_DIR:-$(cd "$REPO_ROOT" && realpath projects)}"
ls "$HOST_PROJECTS/project_${PROJECT_ID}/manual_adjustment/"
# Expected: alignment_data.json, layout.png

If the AMC stack stores project outputs outside the default projects/ directory, set PROJECTS_DIR explicitly before running the check above. Do not read compose/.env for project paths during this workflow.

Step 5 - Verify, Calibrate, Poll, and Fetch Results

Verify:

POST /v1/verify_project/<project_id>

The project must return project_state == "READY".

Confirm the plan before calibrating. Summarize:

  • Stream count and recording duration.
  • Detector: resnet or transformer.
  • Settings source: explicit uploaded settings file, user-provided asset directory, or UI Step 3.
  • Alignment/layout source: explicit uploaded files, user-provided asset directory, or UI manual adjustment.
  • Optional GT and focal-length overrides.

Start calibration:

POST /v1/calibrate/<project_id>
Content-Type: application/json

{"detector_type": "<resnet-or-transformer>"}

Poll:

GET /v1/get_project_info/<project_id>

Stop on COMPLETED or ERROR. On error, fetch GET /v1/amc/calibrate/<project_id>/log.

Fetch results:

GET /v1/result/<project_id>/evaluation_statistics

Only expect evaluation statistics when GT was uploaded.

Step 6 - Optional VGGT Refinement

After AMC calibration completes, read project_info.vggt_state from GET /v1/get_project_info/<project_id>.

  • If vggt_state == "READY", ask whether to run VGGT refinement.
  • If confirmed, call POST /v1/vggt/calibrate/<project_id>, poll vggt_state, then fetch GET /v1/vggt_results/<project_id>/evaluation_statistics.
  • If VGGT is not ready, skip it and explain that AMC calibration is complete.

Complete Python Script

Use the bundled script from the amc-run-rtsp-calibration skill package, not from the auto-magic-calib repo root. If the user points the agent at this skill folder directly instead of installing it, set AMC_RTSP_SKILL_DIR to the directory containing this SKILL.md, or run the command from that directory.

Common environment variables:

bash
export BASE_URL=http://<HOST_IP>:8000
export PROJECT_NAME=rtsp_calibration_run
export RTSP_URLS='rtsp://user:pass@cam0/stream,rtsp://user:pass@cam1/stream'
export CAMERA_NAMES='cam_00,cam_01'
export DURATION_SECONDS=180
export VIOS_BASE_URL=http://<VIOS_HOST>:30888
export CALIB_ASSET_DIR=/path/to/rtsp-calibration-assets
# Or provide explicit CONFIG_FILE, ALIGNMENT_JSON, LAYOUT_PNG, and optional GT_ZIP.
export DETECTOR_TYPE=transformer  # Required when settings do not set detector/detector_type.
export AMC_UI_URL=http://<HOST_IP>:5000
export RUN_VGGT=false

# Optional but recommended: REPO_ROOT points to the auto-magic-calib checkout.
# PROJECTS_DIR can be set explicitly when project outputs live elsewhere.
if [ -z "${DEEPSTREAM_REPO_ROOT:-}" ] && [ -n "${REPO_ROOT:-}" ] && [ -d "$REPO_ROOT/../../skills/amc-run-rtsp-calibration" ]; then
  DEEPSTREAM_REPO_ROOT="$(cd "$REPO_ROOT/../.." && pwd)"
fi

SCRIPT_PATH=""
for candidate in \
  "${AMC_RTSP_SKILL_DIR:+$AMC_RTSP_SKILL_DIR/scripts/run_rtsp_calibration.py}" \
  "$PWD/scripts/run_rtsp_calibration.py" \
  "${DEEPSTREAM_REPO_ROOT:+$DEEPSTREAM_REPO_ROOT/skills/amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py}" \
  "$PWD/skills/amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py" \
  "$HOME/.claude/skills/amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py" \
  "$HOME/.codex/skills/amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py" \
  "$HOME/.cursor/skills/amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py"; do
  if [ -f "$candidate" ]; then
    SCRIPT_PATH="$candidate"
    break
  fi
done

[ -n "$SCRIPT_PATH" ] || {
  echo "ERROR: could not find amc-run-rtsp-calibration/scripts/run_rtsp_calibration.py" >&2
  echo "Set AMC_RTSP_SKILL_DIR to the amc-run-rtsp-calibration skill directory, or run this block from that directory." >&2
  exit 1
}

python3 "$SCRIPT_PATH"

Alternative stream input:

bash
export STREAMS_JSON='[
  {"rtsp_url":"rtsp://cam0/stream","camera_name":"cam_00","sensor_id":null},
  {"rtsp_url":"rtsp://cam1/stream","camera_name":"cam_01","sensor_id":null}
]'

Optional env vars are CALIB_ASSET_DIR, CONFIG_FILE, ALIGNMENT_JSON, LAYOUT_PNG, GT_ZIP, FOCAL_LENGTHS, DETECTOR_TYPE, AMC_UI_URL, SSL_VERIFY, RUN_VGGT, REPO_ROOT, and PROJECTS_DIR. SSL_VERIFY defaults to true; only set SSL_VERIFY=false for loopback testing. If the VIOS deployment requires bearer authentication, stop and use a manual admin-managed capture flow instead of routing credentials through this skill.

Success Criteria

  • VIOS health probe returns 200.
  • Capture session reaches COMPLETED.
  • Ingest returns success and project info shows the expected video files.
  • verify_project returns READY.
  • AMC calibration reaches project_state == "COMPLETED".
  • If GT was uploaded, evaluation statistics are returned.
  • No RTSP credentials, bearer tokens, NGC keys, or HuggingFace tokens are printed or persisted by the agent.

Key Output Files

Results persist on the AMC server under:

projects/project_<project_id>/
|-- manual_adjustment/
|   |-- alignment_data.json
|   `-- layout.png
|-- output/
|   |-- single_view_results/cam_XX/
|   |   |-- camInfo_hyper_XX.yaml
|   |   `-- trajDump_Stream_0_3d.txt
|   `-- multi_view_results/BA_output/results_ba/
|       |-- initial/camInfo_XX.yaml
|       `-- refined/camInfo_XX.yaml
`-- calibration.log

Troubleshooting

IssueFix
VIOS /vst/api/v1/sensor/list returns connection refusedVIOS is not running or not reachable from this host. Ask the user to deploy VIOS or provide the reachable base URL.
Capture endpoint returns 503 or "VIOS not configured"Export VIOS_BASE_URL, relaunch the microservice with the temporary compose override from Step 0, then retry capture.
Session stuck in STARTINGVIOS accepted the request but sensors may not be online. Check ${VIOS_BASE_URL}/vst/api/v1/sensor/list and wait 20-30 seconds after sensor restarts.
Session stuck in RECORDING past duration_secondsCall POST /v1/rtsp/capture/<project_id>/<session_id>/stop, then ingest the partial clip if available.
Ingest fails with "No clip available"The recording window may not overlap the VIOS timeline. Wait for sensors to become online, then start a new capture.
400 "empty streams"Pass at least one stream object with rtsp_url and camera_name.
400 "duration too short"Use duration_seconds >= 60.
404 on /v1/rtsp/capture/<project_id>Create the project first with /v1/create_project.
verify_project is not READY after ingestCheck project info and confirm expected videos, alignment, and layout are attached.
Calibration reaches ERRORFetch GET /v1/amc/calibrate/<project_id>/log; common causes are insufficient tracklets, static scenes, or incorrect alignment.
  • skills/amc-setup-calibration-stack/SKILL.md - start AMC microservice and UI first.
  • skills/amc-run-video-calibration/SKILL.md - calibrate from local pre-recorded MP4 files.
  • skills/amc-run-sample-calibration/SKILL.md - verify the stack with the bundled sample dataset.
<!-- signing marker -->

© NVIDIA, 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 5 other files (scripts) in skills/amc-run-rtsp-calibration of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • scripts/run_rtsp_calibration.py
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 14a98ae

Compare with similar skills

Amc Run Rtsp Calibration 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.

Amc Run Rtsp Calibration compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Amc Run Rtsp Calibration this skillNVIDIA/skills3.6k—~4.6kAutomated safety check: NotesApache-2.0
Binance Datatoollostleaf/binance-datatool148—~2.5kAutomated safety check: NotesBSD-3-Clause
Databricksrocky-data/rocky304—~2kAutomated safety check: PassApache-2.0
PayRam Payment AnalyticsPayRam/payram-mcp158—~4.1kAutomated safety check: PassNone
Trust Wallet APItrustwallet/tw-agent-skills106—~424Automated safety check: PassMIT
Bankr SignalsaAAaqwq/AGI-Super-Team1052 repos~3.3kAutomated safety check: PassMIT

Similar skills

  • Binance Datatool

    lostleaf/binance-datatool

    Manage Binance historical market data from data.binance.vision using the binance-datatool CLI.

    148 GitHub stars~2.5k tokensUpdated 5 mo ago
    Backend & APIsAuto-check: notes
  • Databricks

    rocky-data/rocky

    Databricks REST API and SQL reference for Rocky's warehouse adapter.

    304 GitHub stars~2k tokensUpdated yesterday
    Backend & APIsAuto-check passed
  • PayRam Payment Analytics

    PayRam/payram-mcp

    Queries a PayRam server's dashboard data through its REST APIs with a Bearer token: payment search, daily volume, unswept balances, sweep history and on-ramp metrics.

    158 GitHub stars~4.1k tokensUpdated 2 days ago
    Backend & APIsAuto-check passed
  • Trust Wallet API

    trustwallet/tw-agent-skills

    Trust Wallet API for crypto data — token search, prices, trending tokens, swap quotes, market data, security checks, address validation, asset info, and coin status across 100+ blockchains.

    106 GitHub stars~424 tokensUpdated 4 mo ago
    Backend & APIsAuto-check passed
  • Bankr Signals

    aAAaqwq/AGI-Super-Team

    Transaction-verified trading signals on Base blockchain. An agent skill from aAAaqwq/AGI-Super-Team.

    105 GitHub starsUsed in 2 repos~3.3k tokens
    Backend & APIsAuto-check passed
  • Usfiscaldata

    K-Dense-AI/scientific-agent-skills

    Queries the U.S. An agent skill from K-Dense-AI/scientific-agent-skills.

    48k GitHub starsUsed in 1 repo~2.4k tokens
    Backend & APIsAuto-check: notes

More from NVIDIA/skills

All 390 skills in this repo
  • Official

    A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.

    3.6k GitHub starsUsed in 1 repo~4.5k tokens
    Auto-check passed
  • Official

    Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.

    3.6k GitHub stars~2.9k tokensUpdated yesterday
    Auto-check passed
  • Official

    Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.

    3.6k GitHub stars~4.8k tokensUpdated yesterday
    Auto-check passed
  • Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.

    3.6k GitHub stars~5k tokensUpdated yesterday
    Auto-check: notes
  • Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.

    3.6k GitHub stars~4.7k tokensUpdated yesterday
    Auto-check: notes
  • Official

    Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.

    3.6k GitHub stars~2.7k tokensUpdated yesterday
    Auto-check: notes

Questions about Amc Run Rtsp Calibration

What does Amc Run Rtsp Calibration do?

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Amc Run Rtsp Calibration is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API.

When should I use Amc Run Rtsp Calibration?

Amc Run Rtsp Calibration fits situations like: the user provides RTSP URLs; asks to calibrate live cameras; VIOS records clips; AMC ingests them.

How do I install Amc Run Rtsp Calibration in Claude Code?

Run `npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a claude-code`. Or copy the skill folder (skills/amc-run-rtsp-calibration in NVIDIA/skills) into .claude/skills/amc-run-rtsp-calibration in your project. Claude Code loads it when a task matches its description.

How do I install Amc Run Rtsp Calibration in Codex?

Run `npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a codex`. Or copy the skill folder (skills/amc-run-rtsp-calibration in NVIDIA/skills) into .agents/skills/amc-run-rtsp-calibration in your project. Codex loads it when a task matches its description.

Can I use Amc Run Rtsp Calibration 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/skills --skill amc-run-rtsp-calibration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/amc-run-rtsp-calibration, .gemini/skills/amc-run-rtsp-calibration, .github/skills/amc-run-rtsp-calibration and .opencode/skills/amc-run-rtsp-calibration in your project.

What does Amc Run Rtsp Calibration need to run?

Going by SKILL.md and its folder, Amc Run Rtsp Calibration needs Python for the scripts in its folder and the command-line tools its instructions call (docker, curl and python3). Our summary lists: Python 3; Docker.

Does Amc Run Rtsp Calibration access the network?

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

Is Amc Run Rtsp Calibration safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. 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.

What licence does Amc Run Rtsp Calibration use?

Amc Run Rtsp Calibration 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 Amc Run Rtsp Calibration use?

About 4.6k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Amc Run Rtsp Calibration?

Skills that share tags, products or a category with Amc Run Rtsp Calibration: Binance Datatool (lostleaf/binance-datatool, 148 stars), Databricks (rocky-data/rocky, 304 stars), PayRam Payment Analytics (PayRam/payram-mcp, 158 stars) and Trust Wallet API (trustwallet/tw-agent-skills, 106 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amc Run Rtsp Calibration?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.