Agent Skill

Amc Run Video Calibration skill

amc-run-video-calibrationNVIDIA/skillsskills/amc-run-video-calibration/SKILL.md

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

3.7kApache-2.0

always on

57 tokname + description

once used

4,576 tokSKILL.md body

bundled files

51 scripts

repo stars

3.7kGitHub

Install the Amc Run Video Calibration skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/NVIDIA/skills --skill amc-run-video-calibration

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -R /tmp/skills/skills/amc-run-video-calibration ~/.claude/skills/amc-run-video-calibration

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

When	What loads	Cost
Every session	Name and description	57 tok
When the agent uses it	The SKILL.md instructions	4,576 tok
Only if opened	5 bundled files (43 KB)	varies

Until it is used, amc-run-video-calibration costs about 24× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from NVIDIA/skills would load 20,796 tokens of descriptions. Skills vs MCP study

Checks

Ships scripts the agent can run. Read them before installing.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

Skill: Calibrate from Video Files
When to Use This Skill
Purpose
Prerequisites
Interaction Model
Data Privacy
Inputs
What to Ask the User
Required
Auto-Detected (ask only if not found)
Optional
Available Scripts
Instructions
Step 1 — Create Project
Step 2 — Upload Videos (required)
Step 3 — Resolve Local Files (Auto-Scan, Ask, or UI)
Step 4 — Upload Resolved Files
Step 5 — UI Fallback (only for files the user doesn't have locally)
Step 6 — Verify Project
Step 7 — Start Calibration
Step 8 — Poll for Completion
Step 9 — Get Results
Status Fields from getprojectinfo
Step 10 — (Optional) VGGT Refinement

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