Binance Datatool
lostleaf/binance-datatool
Manage Binance historical market data from data.binance.vision using the binance-datatool CLI.
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API.
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .claude/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibrationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .agents/skills/amc-run-rtsp-calibration && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .agents/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .cursor/skills/amc-run-rtsp-calibration && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .cursor/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/amc-run-rtsp-calibration--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .gemini/skills/amc-run-rtsp-calibration && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .gemini/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibrationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .github/skills/amc-run-rtsp-calibration && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .github/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill amc-run-rtsp-calibration -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills amc-run-rtsp-calibration --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/amc-run-rtsp-calibration .opencode/skills/amc-run-rtsp-calibration && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "amc-run-rtsp-calibration" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/amc-run-rtsp-calibration into .opencode/skills/amc-run-rtsp-calibration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "amc-run-rtsp-calibration", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
amc-run-rtsp-calibrationCalibrate 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. 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.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
dockercurlpython3From the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
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.
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,591 words, ~4,558 tokens.
.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.Activate this skill when the user wants to calibrate from live RTSP camera streams. Typical prompts:
rtsp://... URLsVIOS 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.
skills/amc-setup-calibration-stack/SKILL.md if needed).VIOS_BASE_URL is configured in the AMC microservice environment before capture starts.requests installed when using the bundled script.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.
cam_00, cam_01, ... if the user does not provide names.60; prefer 120-180 or more when the scene has sparse motion.http://<HOST_IP>:8000 or http://<HOST_IP>:8000/v1./data/my_rtsp_calib/;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.
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>?
| File | Candidate filenames | UI fallback |
|---|---|---|
| Calibration settings | Explicit user path, or settings.json, config.json, or calibration_config.json in the user-provided asset directory | UI Step 3: Parameters |
| Alignment JSON | Explicit user path, or alignment_data.json in the user-provided asset directory/settings directory | UI Step 4: Alignment |
| Layout PNG | Explicit user path, or layout.png in the user-provided asset directory/settings directory | UI Step 4: Alignment |
| Ground truth zip | Optional explicit user path, or GT.zip/gt.zip in the user-provided asset directory | Omit 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.
sensor_id per stream if the cameras are already registered in VIOS. Leave unset for auto-registration.GT.zip) for evaluation metrics.vggt_state == "READY".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.
Confirm the AMC microservice is reachable:
curl -sf http://<HOST_IP>:<MS_PORT>/v1/readyConfirm VIOS is reachable before starting capture. Probe in this order and stop at the first working URL:
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$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 ${VIOS_BASE_URL}/vst/api/v1/sensor/list returns 200.
If VIOS is reachable but the AMC microservice is missing VIOS_BASE_URL, do not edit checked-in compose files. Export the variable and relaunch the microservice with a temporary compose override:
cd "$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_URLA 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.
POST /v1/create_project with form field project_name. Save the returned project_id.
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.
Poll every 10 seconds:
GET /v1/rtsp/capture/<project_id>/<session_id>Session lifecycle:
STARTING -> RECORDING -> COMPLETED -> INGESTING -> INGESTED
-> ERROR
RECORDING -> CANCELLEDWhen capture reaches COMPLETED, ingest the recorded clips into the AMC project:
POST /v1/rtsp/capture/<project_id>/<session_id>/ingestAfter 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.Resolve local files using the anchor-file pattern above. Upload resolved files:
| File | Endpoint | Notes |
|---|---|---|
| Calibration settings | POST /v1/config/<project_id> | JSON body posted as-is; replaces UI Step 3 |
| Alignment JSON | POST /v1/upload_alignment/<project_id> | Multipart alignment_file |
| Layout PNG | POST /v1/upload_layout/<project_id> | Multipart layout_file |
| Ground truth zip | POST /v1/upload_gt_file/<project_id> | Optional |
| Focal lengths | POST /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:
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.pngIf 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.
Verify:
POST /v1/verify_project/<project_id>The project must return project_state == "READY".
Confirm the plan before calibrating. Summarize:
resnet or transformer.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_statisticsOnly expect evaluation statistics when GT was uploaded.
After AMC calibration completes, read project_info.vggt_state from GET /v1/get_project_info/<project_id>.
vggt_state == "READY", ask whether to run VGGT refinement.POST /v1/vggt/calibrate/<project_id>, poll vggt_state, then fetch GET /v1/vggt_results/<project_id>/evaluation_statistics.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:
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:
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.
COMPLETED.verify_project returns READY.project_state == "COMPLETED".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| Issue | Fix |
|---|---|
VIOS /vst/api/v1/sensor/list returns connection refused | VIOS 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 STARTING | VIOS 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_seconds | Call 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 ingest | Check project info and confirm expected videos, alignment, and layout are attached. |
Calibration reaches ERROR | Fetch 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
SKILL.md and 5 other files (scripts) in skills/amc-run-rtsp-calibration of NVIDIA/skills.
Open the folder on GitHubat commit 14a98ae
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Amc Run Rtsp Calibration this skillNVIDIA/skills | 3.6k | — | ~4.6k | Automated safety check: Notes | Apache-2.0 | |
| Binance Datatoollostleaf/binance-datatool | 148 | — | ~2.5k | Automated safety check: Notes | BSD-3-Clause | |
| Databricksrocky-data/rocky | 304 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| PayRam Payment AnalyticsPayRam/payram-mcp | 158 | — | ~4.1k | Automated safety check: Pass | None | |
| Trust Wallet APItrustwallet/tw-agent-skills | 106 | — | ~424 | Automated safety check: Pass | MIT | |
| Bankr SignalsaAAaqwq/AGI-Super-Team | 105 | 2 repos | ~3.3k | Automated safety check: Pass | MIT |
lostleaf/binance-datatool
Manage Binance historical market data from data.binance.vision using the binance-datatool CLI.
rocky-data/rocky
Databricks REST API and SQL reference for Rocky's warehouse adapter.
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.
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.
aAAaqwq/AGI-Super-Team
Transaction-verified trading signals on Base blockchain. An agent skill from aAAaqwq/AGI-Super-Team.
K-Dense-AI/scientific-agent-skills
Queries the U.S. An agent skill from K-Dense-AI/scientific-agent-skills.
NVIDIA/skills
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.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
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.
NVIDIA/skills
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.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
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.
Categories
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.
Amc Run Rtsp Calibration fits situations like: the user provides RTSP URLs; asks to calibrate live cameras; VIOS records clips; AMC ingests them.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.