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Get Started Free →nvidia/amc-run-sample-calibration
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice.
Passed 18 of 20 test cases with the skill, and 4 of 20 without it.
and 19 more in the full report
17 fixed3 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
What detector_type configuration value should be sent in the JSON body when calling POST /v1/calibrate/{project_id} for the standard sample dataset?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies JSON body setting detector_type to resnet. | Missed | Passed |
The instructions your model reads when the skill loads.
Activate this skill when the user wants to sanity-check a running AMC stack with the bundled sample dataset. Typical prompts:
amc-setup-calibration-stack if the MS isn't already running)Do NOT use this skill when:
/data/videos/, cam_*.mp4 not from the bundled zip) — route to amc-run-video-calibration.rtsp://... URLs — route to amc-run-rtsp-calibration.assets/sdg_08_2_sample_data_010926.zip.Prerequisite: AMC microservice running on a port in 8000-8009. If no backend is detected, delegate to amc-setup-calibration-stack first.
If execution cannot proceed in the current environment (no backend, missing sample data, etc.), surface the blocker AND describe the expected workflow + API sequence concisely so the user understands what will run once prerequisites are met. Do not fabricate calibration outputs, evaluation metrics, or trajectories.
Run a full calibration on the bundled sample dataset (sdg_08_2_sample_data_010926.zip, 4 synthetic warehouse cameras with ground truth) against a running AutoMagicCalib microservice. Useful for verifying that a freshly-launched stack works end-to-end before throwing real data at it.
The sample includes GT, so the run produces evaluation metrics (L2 distance, reprojection error) — no calibration parameter tuning needed.
skills/amc-setup-calibration-stack/SKILL.md if not)assets/sdg_08_2_sample_data_010926.ziprequests available, or use the Swagger UI path below
requests is missing it creates a throwaway venv under ${TMPDIR:-/tmp}/amc-sample-test-venv (nothing written to the repo)python3 -m venv itself fails with ensurepip not available: sudo apt install -y python3-venv python3-pip"launch AMC and test sample dataset" (or similar):
skills/amc-setup-calibration-stack/SKILL.md first./v1/ready to return OK.test cases passed, vs 4 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.