Official agent skill

Amc Setup Calibration Stack

by NVIDIA in NVIDIA/skills

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose.

OfficialApache-2.0Auto-check: notesDevOps & Cloud

Install Amc Setup Calibration Stack

skills CLI
$ npx skills add NVIDIA/skills --skill amc-setup-calibration-stack -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills amc-setup-calibration-stack --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-setup-calibration-stack .claude/skills/amc-setup-calibration-stack && 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-setup-calibration-stack
GitHub stars
3.5k
Token cost
~3.9k tokens
SKILL.md length
789 words
Files
5
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose.

  • Works in 7 steps: Verify Docker Runs Without sudo → Login to NGC → Download VGGT Model (If Not Already… → …
  • User says deploy auto calibration
  • SKILL.md covers Prerequisites, Instructions, Success Criteria and Troubleshooting, plus 2 more sections
  • Calls docker, git and python3; reaches github.com; needs HF_TOKEN and NGC_API_KEY

What it does

Amc Setup Calibration Stack is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).

It sits in DevOps & Cloud, covering Performance reviews, Containers and Microservices. It works with Docker and NVIDIA AI Platform. 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

  • User says deploy auto calibration
  • Launch auto calibration
  • Set up auto-magic-calib

Example prompts

  • “deploy auto calibration”
  • “launch auto calibration”
  • “launch AMC”
  • “/amc-setup-calibration-stack”

Requirements

  • Python 3
  • Docker
  • A credential in NGC_API_KEY

Workflow steps

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

  1. Verify Docker Runs Without sudo
  2. Login to NGC
  3. Download VGGT Model (If Not Already Present)
  4. Configure Compose Environment Variables
  5. Set Directory Permissions
  6. Launch Services
  7. Verify Services Are Running

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. 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

    Shell commands in SKILL.md call:

    • docker
    • git
    • python3
    • curl

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • huggingface.co

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • HF_TOKEN
    • NGC_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Amc Setup Calibration Stack loads about 3.9k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 789 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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.

  • NoteRuns commands with sudoSKILL.md:36
    sudo usermod -aG docker $USER && newgrp docker
  • NoteRuns commands with sudoSKILL.md:107
    echo "Install it manually: sudo apt install -y python3-venv python3-pip" >&2
  • NoteRuns commands with sudoSKILL.md:246
    xplicit user confirmation before running sudo chown — it recursively changes
  • NoteRuns commands with sudoSKILL.md:255
    sudo chown 1000:1000 -R projects
  • NoteRuns commands with sudoSKILL.md:256
    sudo chown 1000:1000 -R models
  • NoteRuns commands with sudoSKILL.md:351
    permission denied | Ask the user to run `sudo usermod -aG docker $USER && newgrp docker`, then retry `docker ps`. |

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 789 words, ~3,933 tokens.

Download SKILL.mdSave it as .claude/skills/amc-setup-calibration-stack/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
amc-setup-calibration-stack
description
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
metadata.author
NVIDIA CORPORATION
metadata.tags
amc, deepstream, docker, calibration, setup, ngc
owner
NVIDIA CORPORATION
service
auto-magic-calib
version
1.0.0
reviewed
2026-04-28
license
Apache-2.0

Skill: Launch AutoMagicCalib Release Containers

Set up the AutoMagicCalib microservice and UI from release containers: resolve an AMC checkout, authenticate to NGC, optionally download VGGT, configure Docker Compose, launch services, and verify readiness.

Prerequisites

  • Docker and Docker Compose installed
  • NVIDIA Docker Runtime configured (for GPU support)
  • auto-magic-calib repo on disk. Step 0b resolves the current repo, DeepStream tools/auto-magic-calib, DEEPSTREAM_REPO_ROOT, or ~/auto-magic-calib; otherwise it asks before cloning https://github.com/NVIDIA-AI-IOT/auto-magic-calib.
  • NGC account with access to NVIDIA container registry
  • Docker runnable without sudo; verify with docker ps before continuing.

Instructions

Step 0: Verify Docker Runs Without sudo
bash
docker ps
  • If it succeeds → continue.
  • If it fails with "permission denied" → the user is not in the docker group. Ask the user to run:
    bash
    sudo usermod -aG docker $USER && newgrp docker
    Then ask the user to confirm docker ps works before continuing.

Agent note: If docker ps cannot be run from within the agent sandbox, ask the user to confirm it works (e.g. "Can you confirm docker ps runs without sudo?") before proceeding.

Step 0b: Resolve Repo Checkout

The skill needs AMC repo assets (compose/, sample data, and models/). Resolve an existing checkout first; ask before cloning into ~/auto-magic-calib.

bash
REPO_URL="https://github.com/NVIDIA-AI-IOT/auto-magic-calib.git"
DEFAULT_CLONE_DIR="$HOME/auto-magic-calib"
CURRENT_GIT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || true)"

is_amc_checkout() {
  [ -n "$1" ] \
    && [ -f "$1/README.md" ] \
    && grep -q "AutoMagicCalib" "$1/README.md" 2>/dev/null \
    && [ -f "$1/compose/compose.yml" ] \
    && grep -q "auto-magic-calib-ms" "$1/compose/ms/compose.yml" 2>/dev/null \
    && grep -q "auto-magic-calib-ui" "$1/compose/ui/compose.yml" 2>/dev/null
}

REPO_ROOT=""
for candidate in \
  "$CURRENT_GIT_ROOT" \
  "${CURRENT_GIT_ROOT:+$CURRENT_GIT_ROOT/tools/auto-magic-calib}" \
  "${DEEPSTREAM_REPO_ROOT:+$DEEPSTREAM_REPO_ROOT/tools/auto-magic-calib}" \
  "$PWD/tools/auto-magic-calib" \
  "$DEFAULT_CLONE_DIR"; do
  if is_amc_checkout "$candidate"; then
    REPO_ROOT="$candidate"
    echo "✓ Using auto-magic-calib checkout: $REPO_ROOT"
    break
  fi
done

if [ -z "$REPO_ROOT" ]; then
  if [ -n "$CURRENT_GIT_ROOT" ] && [ -d "$CURRENT_GIT_ROOT/tools/auto-magic-calib" ]; then
    echo "Found $CURRENT_GIT_ROOT/tools/auto-magic-calib, but it is not an initialized AMC checkout."
    echo "If running from the DeepStream repository root:"
    echo "  git submodule update --init tools/auto-magic-calib"
  fi

  # Nothing usable on disk — STOP and ask the user for confirmation using the
  # host's question mechanism; if none is available, ask in chat and wait.
  # Do NOT clone silently from this block or clone over a tracked submodule path.
  echo "No usable auto-magic-calib checkout found. Ask the user for confirmation:"
  echo "  Clone $REPO_URL into $DEFAULT_CLONE_DIR? [y/N]"
  echo "On 'y' — run: git clone \"$REPO_URL\" \"$DEFAULT_CLONE_DIR\""
  exit 1
fi

cd "$REPO_ROOT"
export REPO_ROOT
echo "REPO_ROOT=$REPO_ROOT"

Agent note: never clone silently. Prefer initialized DeepStream tools/auto-magic-calib; do not clone over that submodule path. If it exists but is empty, ask the user to run git submodule update --init tools/auto-magic-calib. Honour an alternate AMC path if provided.

Step 0c: Install Python venv (New Systems Only)

On a fresh system, pip and python3-venv may not be available. Install them first:

bash
# Create a venv for HuggingFace CLI (project-local preferred)
REPO_DIR="$(git rev-parse --show-toplevel 2>/dev/null || pwd)"
HF_VENV="${REPO_DIR}/venv"
python3 -m venv "$HF_VENV" 2>/dev/null || {
  echo "ERROR: python3-venv not available." >&2
  echo "Install it manually: sudo apt install -y python3-venv python3-pip" >&2
  exit 1
}

# Install HuggingFace hub (needed for VGGT download)
"$HF_VENV/bin/pip" install --upgrade pip huggingface_hub

Note: Skip this step if a venv with hf already exists (check venv/bin/hf in the repo root or ~/venv/amc/bin/hf).

Step 1: Login to NGC

Ask the user for their NGC API key using the host's question mechanism; if none is available, ask in chat and wait. Then run:

bash
echo "<NGC_API_KEY>" | docker login nvcr.io --username '$oauthtoken' --password-stdin
echo "✓ NGC authentication complete"
Step 2: Download VGGT Model (If Not Already Present)
bash
export REPO_ROOT=$(git rev-parse --show-toplevel)
cd "$REPO_ROOT"

if [ -f "models/vggt/vggt_1B_commercial.pt" ]; then
  echo "✓ VGGT model already present"
else
  echo "✗ VGGT model not found"
  echo "Options:"
  echo "  1. Continue without VGGT (AMC only - sufficient for most use cases)"
  echo "  2. Download VGGT model (~4.7GB, requires HuggingFace account)"
fi

To download VGGT: ask the user to accept the license at https://huggingface.co/facebook/VGGT-1B-Commercial and provide a read token from https://huggingface.co/settings/tokens using the host's question mechanism. Pass it through HF_TOKEN so it is not exposed in ps output:

bash
REPO_DIR="$(git rev-parse --show-toplevel 2>/dev/null || pwd)"
cd "$REPO_DIR"

# Find the HuggingFace CLI binary (named 'hf', not 'huggingface-cli')
HF_BIN="$(find "$REPO_DIR/venv" ~/venv/amc -name hf -type f 2>/dev/null | head -1)"
{ [ -z "$HF_BIN" ] || [ ! -x "$HF_BIN" ]; } && { echo "ERROR: hf binary not found or not executable; install the hf CLI (Step 0c) or set HF_BIN" >&2; exit 1; }

# Do NOT use --token on the command line (leaks via ps/argv). The HF CLI
# reads HF_TOKEN from the environment automatically.
HF_TOKEN="<HF_TOKEN>" "$HF_BIN" download facebook/VGGT-1B-Commercial \
  --local-dir models/vggt/

# Verify
ls -lh models/vggt/vggt_1B_commercial.pt
# Should show ~4.7GB file

Important: Download BEFORE setting chown 1000:1000 on the models directory — the current user needs write access during download. Set permissions in Step 4 after download completes.

Step 3: Configure Compose Environment Variables

The Compose environment file controls ports and paths. Update it before launching:

bash
cd $REPO_ROOT/compose

# Find available backend port (8000-8009)
for port in {8000..8009}; do
  if ! lsof -Pi :$port -sTCP:LISTEN -t >/dev/null 2>&1; then
    MS_PORT=$port
    echo "Using backend port: $MS_PORT"
    break
  fi
done
[ -z "$MS_PORT" ] && { echo "ERROR: no free backend port in 8000-8009; free one or widen the range." >&2; exit 1; }

# Find available UI port (5000-5009)
for port in {5000..5009}; do
  if ! lsof -Pi :$port -sTCP:LISTEN -t >/dev/null 2>&1; then
    UI_PORT=$port
    echo "Using UI port: $UI_PORT"
    break
  fi
done
[ -z "$UI_PORT" ] && { echo "ERROR: no free UI port in 5000-5009; free one or widen the range." >&2; exit 1; }

# Get host IP
HOST_IP=$(hostname -I | awk '{print $1}')
echo "Host IP: $HOST_IP"

# Preserve existing keys and restrict permissions on the Compose environment file.
COMPOSE_ENV_BASENAME="env"
ENV_FILE=".${COMPOSE_ENV_BASENAME}"
if [ -f "$ENV_FILE" ]; then
  BACKUP="${ENV_FILE}.bak.$(date +%s)"
  cp "$ENV_FILE" "$BACKUP"
  chmod 600 "$BACKUP"
fi
touch "$ENV_FILE"
chmod 600 "$ENV_FILE"
set_env_key() {
  local k="$1" v="$2"
  if grep -qE "^${k}=" "$ENV_FILE"; then
    sed -i "s|^${k}=.*|${k}=${v}|" "$ENV_FILE"
  else
    echo "${k}=${v}" >> "$ENV_FILE"
  fi
}
set_env_key AUTO_MAGIC_CALIB_MS_PORT "${MS_PORT}"
set_env_key AUTO_MAGIC_CALIB_UI_PORT "${UI_PORT}"
set_env_key PROJECT_DIR "../../projects"
set_env_key MODEL_DIR "../../models"
set_env_key HOST_IP "${HOST_IP}"

# Keep timestamped Compose environment backups out of git.
GITIGNORE="$REPO_ROOT/.gitignore"
touch "$GITIGNORE"
BACKUP_PATTERN="compose/${ENV_FILE}.bak.*"
grep -qxF "$BACKUP_PATTERN" "$GITIGNORE" || echo "$BACKUP_PATTERN" >> "$GITIGNORE"

echo "✓ Compose environment file updated"
cat "$ENV_FILE"

Important: HOST_IP must be the machine's network IP (not localhost) so the UI container can reach the backend from a browser.

Optional: set VGGT_MODEL_PATH only if the VGGT model is mounted at a non-default container path; default is /tmp/vggt_model/vggt_1B_commercial.pt inside the MS container.

Optional for RTSP calibration: use skills/amc-run-rtsp-calibration/SKILL.md after launch. That skill verifies VIOS reachability and, when needed, relaunches the microservice with a temporary compose override that exports VIOS_BASE_URL without changing checked-in compose files.

Step 4: Set Directory Permissions

The containers run as UID/GID 1000. The projects and models directories must be owned by this UID for containers to read/write properly:

bash
cd "$REPO_ROOT"

# Create projects directory if it doesn't exist
mkdir -p projects

# Set ownership (required for containers to write calibration outputs).
# Do this AFTER VGGT download is complete (current user needs write access during download).
# Get explicit user confirmation before running sudo chown — it recursively changes
# ownership of $REPO_ROOT/projects and $REPO_ROOT/models to UID/GID 1000.
[ -d projects ] && [ -d models ] || {
  echo "ERROR: expected projects/ and models/ under $REPO_ROOT" >&2; exit 1;
}
echo "About to chown -R 1000:1000 on:"
echo "  $REPO_ROOT/projects"
echo "  $REPO_ROOT/models"
echo "(required because containers run as UID 1000). Confirm before proceeding."
sudo chown 1000:1000 -R projects
sudo chown 1000:1000 -R models

echo "✓ Permissions set"
Show full SKILL.md (307 more words)Show less
Step 5: Launch Services

Before pulling, fail fast if the NGC key authenticated in Step 1 but cannot actually access a release image — otherwise docker compose up aborts partway with a 401/403 after some work is already done.

bash
cd $REPO_ROOT/compose

# Fail-fast image-access check: confirm the NGC key can reach every release
# image BEFORE pulling. `docker manifest inspect` checks registry access without
# downloading layers, and the image list is read from the resolved compose so it
# tracks the release tag automatically.
IMAGES=$(docker compose config --images | sort -u)
[ -z "$IMAGES" ] && { echo "ERROR: no images resolved from compose — check the Compose environment settings and chosen profile." >&2; exit 1; }
for img in $IMAGES; do
  echo "Checking access: $img"
  if ! docker manifest inspect "$img" >/dev/null 2>&1; then
    echo "NGC login succeeded, but this key cannot access the required image:" >&2
    echo "  $img" >&2
    echo "Provide an NGC key with access to this image's namespace, then re-run Step 1 (login) and retry." >&2
    exit 1
  fi
done

# Start all services (images pulled automatically on first run)
docker compose up -d

# Check containers are running
docker compose ps

The exact image tags change by release; read them from the active compose files instead of hardcoding a version.

Step 6: Verify Services Are Running
bash
# Read ports from the Compose environment file.
COMPOSE_ENV_BASENAME="env"
COMPOSE_ENV_FILE="$REPO_ROOT/compose/.${COMPOSE_ENV_BASENAME}"
MS_PORT=$(grep AUTO_MAGIC_CALIB_MS_PORT "$COMPOSE_ENV_FILE" | cut -d= -f2)
UI_PORT=$(grep AUTO_MAGIC_CALIB_UI_PORT "$COMPOSE_ENV_FILE" | cut -d= -f2)
HOST_IP=$(grep HOST_IP "$COMPOSE_ENV_FILE" | cut -d= -f2)

# Wait for microservice readiness. Cold image pulls or first startup can need
# extra time after `docker compose up -d` returns.
READY_URL="http://localhost:${MS_PORT}/v1/ready"
echo "Waiting for microservice readiness at ${READY_URL} ..."
ready_response=""
for attempt in $(seq 1 24); do
  if ready_response=$(curl -fsS --max-time 5 "${READY_URL}" 2>/dev/null) && \
     echo "${ready_response}" | grep -q '"code"[[:space:]]*:[[:space:]]*0'; then
    echo "Microservice ready: ${ready_response}"
    break
  fi
  if [ "${attempt}" -lt 24 ]; then
    printf "  [%02d/24] Microservice not ready yet; retrying in 5s...\n" "${attempt}"
    sleep 5
  fi
done

if ! echo "${ready_response}" | grep -q '"code"[[:space:]]*:[[:space:]]*0'; then
  echo "ERROR: microservice did not report ready within 120 seconds: ${READY_URL}" >&2
  echo "Check status and logs:" >&2
  echo "  cd ${REPO_ROOT}/compose && docker compose ps" >&2
  echo "  cd ${REPO_ROOT}/compose && docker compose logs auto-magic-calib-ms" >&2
  exit 1
fi

# Check UI is serving
UI_STATUS=$(curl -s -o /dev/null -w "%{http_code}" --max-time 5 "http://localhost:${UI_PORT}")
if [ "${UI_STATUS}" != "200" ]; then
  echo "ERROR: Web UI returned HTTP ${UI_STATUS}; check docker compose ps and UI logs." >&2
  exit 1
fi
echo "Web UI ready: HTTP ${UI_STATUS}"

echo "Microservice: http://${HOST_IP}:${MS_PORT}"
echo "Web UI:       http://${HOST_IP}:${UI_PORT}"

Success Criteria

  • docker compose ps shows MS and UI containers Up; MS should be healthy.
  • /v1/ready returns code:0 and Step 6 prints the microservice and UI URLs.
  • Browser access to http://<HOST_IP>:<AUTO_MAGIC_CALIB_UI_PORT> works.
  • Projects persist under $REPO_ROOT/projects/.

Troubleshooting

IssueFix
Docker permission deniedAsk the user to run sudo usermod -aG docker $USER && newgrp docker, then retry docker ps.
docker login rejectedAsk for a current NGC key and log in again.
Required image inaccessibleThe key lacks image namespace access; ask for a key with access, then retry Step 1 and Step 5.
python3 -m venv, pip, or hf missingInstall python3-venv/python3-pip; the HF binary is named hf.
VGGT permission errorDownload VGGT before chown 1000:1000; to recover, restore user ownership of models/ and re-download.
Port in usePick a free MS port in 8000-8009 and UI port in 5000-5009, then update the Compose environment file.
Readiness timeout or exited containerRun cd $REPO_ROOT/compose && docker compose ps and inspect docker compose logs auto-magic-calib-ms.
Project/model permission deniedRe-run Step 4 for projects/ or models/ only.
UI cannot reach backendVerify HOST_IP in the Compose environment file is the machine network IP, not localhost.
GPU unavailableVerify NVIDIA runtime with docker run --rm --runtime=nvidia --gpus all ubuntu:20.04 nvidia-smi.

Common Fixes:

bash
cd $REPO_ROOT/compose

# View logs
docker compose logs -f

# View logs for specific service
docker compose logs -f auto-magic-calib-ms

# Restart all services
docker compose restart

# Stop and remove containers
docker compose down

# Update Compose environment settings and relaunch
docker compose up -d

Stopping the Services

bash
cd $REPO_ROOT/compose

# Stop all services (containers removed, data persisted)
docker compose down

# Stop and remove volumes
docker compose down -v
  • skills/amc-run-sample-calibration/SKILL.md - Sanity-check the running stack with the bundled sample dataset
  • skills/amc-run-video-calibration/SKILL.md - Calibrate from your own pre-recorded MP4s via REST API
  • skills/amc-run-rtsp-calibration/SKILL.md - Calibrate from live RTSP streams through VIOS capture
<!-- 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 4 other files in skills/amc-setup-calibration-stack of NVIDIA/skills.

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

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Amc Setup Calibration Stack 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.

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Model Download Devopen-edge-platform/edge-ai-libraries168—~2kAutomated safety check: PassApache-2.0
Time Series Analytics Devopen-edge-platform/edge-ai-libraries168—~1.5kAutomated safety check: NotesApache-2.0
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Questions about Amc Setup Calibration Stack

What does Amc Setup Calibration Stack do?

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Amc Setup Calibration Stack is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose.

When should I use Amc Setup Calibration Stack?

Amc Setup Calibration Stack fits situations like: user says deploy auto calibration; launch auto calibration; set up auto-magic-calib.

How do I install Amc Setup Calibration Stack in Claude Code?

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

How do I install Amc Setup Calibration Stack in Codex?

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

Can I use Amc Setup Calibration Stack 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-setup-calibration-stack -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-setup-calibration-stack, .gemini/skills/amc-setup-calibration-stack, .github/skills/amc-setup-calibration-stack and .opencode/skills/amc-setup-calibration-stack in your project.

What does Amc Setup Calibration Stack need to run?

Going by SKILL.md and its folder, Amc Setup Calibration Stack needs the command-line tools its instructions call (docker, git, python3 and curl) and credentials named HF_TOKEN and NGC_API_KEY. Our summary lists: Python 3; Docker; A credential in NGC_API_KEY.

Does Amc Setup Calibration Stack access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: huggingface.co. This is read from the text; nothing was executed.

Is Amc Setup Calibration Stack safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Amc Setup Calibration Stack use?

Amc Setup Calibration Stack 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 Setup Calibration Stack use?

About 3.9k tokens (SKILL.md is roughly 16k 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 Setup Calibration Stack?

Skills that share tags, products or a category with Amc Setup Calibration Stack: Frontmcp Deployment (agentfront/frontmcp, 146 stars), Model Download Dev (open-edge-platform/edge-ai-libraries, 168 stars), Time Series Analytics Dev (open-edge-platform/edge-ai-libraries, 168 stars) and Supabase (magnus919/agent-skills, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amc Setup Calibration Stack?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 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.