[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PMF6MYW6SDW2FEHEC3SR8S":3,"skill-same:01M6PMF6MYW6SDW2FEHEC3SR8S":122},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6PMF6MYW6SDW2FEHEC3SR8S","vss-generate-video-calibration",null,"Use to run AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, and to deploy vss-auto-calibration when needed. Do not use for non-AMC calibration or runtime analytics.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fvss-generate-video-calibration\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"generate video","vss generate video calibration","calibration","Video","generate","search","https","NVIDIA","run","use","auto","Blueprint","do","summarization","local","sample","runtime","dataset","for","github","2026-11-10 10:12:30",false,"---\nname: vss-generate-video-calibration\ndescription: Use to run AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, and to deploy vss-auto-calibration when needed. Do not use for non-AMC calibration or runtime analytics.\nlicense: Apache-2.0\nmetadata:\n  version: \"3.2.0\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization\"\n  tags: \"nvidia blueprint operational\"\n---\n## Purpose\n\nRun AutoMagicCalib end-to-end on local files, RTSP streams, or the bundled sample dataset and (when needed) deploy the AMC microservice.\n\n## Instructions\n\nFollow the routing tables and step-by-step workflows below. Each section that ends in *workflow*, *quick start*, or *flow* is intended to be executed top-to-bottom. Detailed reference material lives in `references\u002F`; load only the reference needed for the selected input mode.\n\n## Examples\n\nWorked end-to-end examples are kept under `evals\u002F` (each `*.json` manifest contains a runnable scenario) and inline in the per-workflow `curl` blocks below. Run a Tier-3 evaluation with `nv-base validate \u003Cthis-skill-dir> --agent-eval` to replay them.\n\n## Limitations\n\n- Requires the matching VSS profile \u002F microservice to be deployed and reachable from the caller.\n- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.\n- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.\n\n## Troubleshooting\n\n- **Error**: REST call returns connection refused. **Cause**: target microservice not running. **Solution**: probe `\u002Fdocs` or `\u002Fhealth`; redeploy via `vss-deploy-profile` or the matching `vss-deploy-*` skill.\n- **Error**: HTTP 401\u002F403 from NGC pulls. **Cause**: missing\u002Fexpired `NGC_CLI_API_KEY`. **Solution**: `docker login nvcr.io` and re-export the key before retrying.\n- **Error**: container OOM or model fails to load. **Cause**: insufficient GPU memory for the selected profile. **Solution**: switch to a smaller variant or free GPUs via `docker compose down`.\n\n# VSS Generate Video Calibration\n\nRun AutoMagicCalib over one of three input sources and drive the calibration through the microservice REST API. The input-resolution work differs per source; everything from `verify_project` onward is identical and lives in this file. Pick the right input-mode reference and pair it with the [Shared Calibration Tail](#shared-calibration-tail) below.\n\nShared helper references are loaded only when needed:\n- Read [`references\u002Fcommon-steps.md`](references\u002Fcommon-steps.md) when a mode reference needs the shared `create_project`, video-upload, or handoff snippets.\n- Read [`references\u002Fcalibration-tail.md`](references\u002Fcalibration-tail.md) when you need the reusable Python implementation of the verify → calibrate → poll → results tail.\n\n## Input Routing\n\nMatch the user's request to a mode, then load that mode's reference for input collection, mode-specific API calls, and the full Python script.\n\n| User says \u002F has | Mode | Reference |\n|---|---|---|\n| \"launch AMC\" \u002F \"deploy auto-calibration\" \u002F \"set up auto-magic-calib\" \u002F \"start AMC microservice\" | `deploy` | [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) |\n| \"calibrate my videos\" \u002F \"calibrate from video files\" \u002F local `cam_*.mp4` files | `videos` | [`references\u002Fvideos.md`](references\u002Fvideos.md) |\n| \"calibrate RTSP streams\" \u002F \"calibrate from live cameras\" \u002F live RTSP URLs | `rtsp` | [`references\u002Frtsp.md`](references\u002Frtsp.md) |\n| \"test sample dataset\" \u002F \"verify AMC install\" \u002F \"launch and test\" | `sample-dataset` | [`references\u002Fsample-dataset.md`](references\u002Fsample-dataset.md) |\n\n**Disambiguation rule:** if the user is asking to launch \u002F deploy \u002F set up AMC (no calibration verb) → `deploy`. If they provide RTSP URLs → `rtsp`. If they mention local files \u002F a videos directory → `videos`. If they ask to verify install or test the bundled sample → `sample-dataset`. Combined intents (e.g. \"launch AMC and calibrate my videos\") → walk `deploy` first, then the calibration mode. When ambiguous, ask via `AskUserQuestion`.\n\n## Prerequisites (shared across calibration modes)\n\n- AMC microservice + UI running. If not, walk [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) first.\n- Microservice reachable at `http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_PORT:-8010}\u002Fv1\u002Fready` → `{\"code\":0,...}`.\n- Projects directory writable by the container user. If you didn't just deploy (so Step 5 of the deploy reference hasn't run), confirm the write test in [`references\u002Fdeploy-auto-calibration-service.md` § Step 5](references\u002Fdeploy-auto-calibration-service.md#step-5--confirm-the-projects-directory-is-writable) — otherwise the first `create_project` returns `[Errno 13] Permission denied`.\n- Python 3 with `requests` installed (each input-mode reference includes a self-healing venv fallback for direct runs).\n\nMode-specific prerequisites (VIOS for `rtsp`, sample zip for `sample-dataset`) live in the respective references.\n\n## Shared Calibration Tail\n\nThe verify → calibrate → poll → results sequence is identical regardless of input mode. After the mode-specific reference has uploaded videos \u002F ingested RTSP clips \u002F uploaded the bundled sample, run this tail. Use [`references\u002Fcalibration-tail.md`](references\u002Fcalibration-tail.md) for the shared Python snippet.\n\n### Step A — Verify Project\n\n```\nPOST \u002Fv1\u002Fverify_project\u002F\u003Cproject_id>\n```\n\nResponse: `{\"project_state\": \"READY\"}` — must be `READY` before calibrating. If not READY, re-check that videos + alignment + layout are present (either via API or via UI manual alignment).\n\n### Step B — Start Calibration\n\n**Confirm the plan before calibrating.** Whether the settings file and detector were auto-detected or asked, present a short summary and confirm via `AskUserQuestion` before the `POST \u002Fcalibrate`. The resolved values are the defaults, so confirming is one click — but the user can switch the detector or skip an auto-detected settings file. Summarize:\n\n- **Detector** — `resnet` or `transformer` (the value to be sent).\n- **Calibration settings** — the file being applied (path), or default parameters (with the option to tune them in the UI first — see below).\n- **Optional overrides** — ground-truth zip and focal lengths, if any.\n\nThe sample-dataset install-check run uses a fixed `resnet` and can proceed without this confirmation.\n\n```\nPOST \u002Fv1\u002Fcalibrate\u002F\u003Cproject_id>\nContent-Type: application\u002Fjson\n\n{\"detector_type\": \"resnet\"}   # or \"transformer\"\n```\n\n`detector_type` is a separate `\u002Fcalibrate` parameter — **not** consumed by `\u002Fv1\u002Fconfig\u002F\u003Cid>`. If the user provided a calibration settings file, parse it for `\"detector\"` \u002F `\"detector_type\"` and use that value. If the file doesn't specify one, the default (`resnet`) is the value shown in the confirmation above — the user can switch it there before calibrating. If there's no settings file at all, ask the user via `AskUserQuestion`:\n\n- `resnet` — default, fast.\n- `transformer` — slower, better under heavy occlusion.\n\nUI Step 3 (Parameters) does NOT cover detector choice; never assume the user picked one in the UI.\n\n**Also when there's no settings file, ask whether to tune the calibration parameters first** (`AskUserQuestion`):\n\n- **Proceed with the default parameters** — well-suited to typical warehouse scenes; recommended unless the user has specific tuning in mind.\n- **Adjust parameters in the UI first** — open the project, go to Step 3: Parameters, change values, and click Save; then continue.\n\nWait for the user's choice — and, if they choose to tune, for them to confirm they've Saved — before calling `\u002Fcalibrate`.\n\n### Step C — Poll for Completion\n\n```\nGET \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>\n```\n\nPoll every 10 s. `project_info.project_state`:\n\n| State | Meaning |\n|---|---|\n| `RUNNING` | Calibration in progress |\n| `COMPLETED` | Finished |\n| `ERROR` | Failed — pull log via `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` |\n\nWhen calibration starts, surface the project ID, the UI URL (`http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_UI_PORT:-5000}`), and the log endpoint so the user can watch progress while the run proceeds. During `RUNNING`, emit a progress line at least once a minute with elapsed time so a long run doesn't look stalled. On `ERROR`, fetch and show the last lines of `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` before stopping. Live logs can also be streamed via `GET \u002Fv1\u002Fcalibrate\u002F\u003Cproject_id>\u002Flog\u002F\u003Ctype>\u002Fstream`.\n\nTypical time: **10–60 min** (your-own videos), **10–30 min** (bundled sample).\n\n### Step D — Results\n\n```\nGET \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>                    # project state\nGET \u002Fv1\u002Fresult\u002F\u003Cproject_id>\u002Fevaluation_statistics        # only if GT uploaded\nGET \u002Fv1\u002Fresult\u002F\u003Cproject_id>\u002Foverlay_image                # visual overlay (PNG)\nGET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cproject_id>\u002Flog                   # calibration log\n```\n\nEvaluation response includes `Average L2 distance(m)` and `Average reprojection error 0(px)`. Evaluation metrics are produced **only when a ground-truth `GT.zip` was uploaded** — a missing `evaluation_statistics` result is normal otherwise and is not the end of result reporting.\n\nAfter `COMPLETED`, always give the user a way to review the result for that exact project, regardless of whether metrics exist:\n\n- **UI** — `http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_UI_PORT:-5000}`; open the project, then the Results page to view the overlay.\n- **Overlay image on disk** — `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Foutput\u002Fmulti_view_results\u002FBA_output\u002Fresults_ba_scaled_world\u002Foverlay_img_*.png` (single-camera projects use `output\u002Fsingle_view_results\u002Fcam_00\u002Fverification_map_overlay.png`).\n- **Project files** — `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002F`.\n\n### Step E — VGGT Refinement\n\nAfter the AMC run completes, always check `vggt_state` in project info. VGGT model staging is optional during setup and must not block the AMC result, but post-AMC handling follows the state:\n\n- If `vggt_state == \"READY\"` and the user explicitly requested VGGT refinement or staged VGGT during this setup flow, run VGGT refinement without asking again.\n- If `vggt_state == \"READY\"` but VGGT was already staged before this request and the user has not asked for VGGT-refined output, ask via `AskUserQuestion` whether to run refinement before starting it.\n- If VGGT is not ready, skip refinement and mention that VGGT refinement is available after staging the model (see [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) Step 2).\n\n```\nPOST \u002Fv1\u002Fvggt\u002Fcalibrate\u002F\u003Cproject_id>\nGET  \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>                    # poll vggt_state\nGET  \u002Fv1\u002Fvggt_results\u002F\u003Cproject_id>\u002Fevaluation_statistics  # VGGT metrics\n```\n\n## Settings File + Detector Pattern\n\nOptional across all three modes. When the user provides a JSON settings file (typically exported from UI Step 3 Download), POST it verbatim:\n\n```\nPOST \u002Fv1\u002Fconfig\u002F\u003Cproject_id>\nContent-Type: application\u002Fjson\n\n\u003Cfile contents, posted as-is>\n```\n\nThe file replaces what the user would otherwise tune in UI Step 3 (rectification, bundle-adjustment, evaluation knobs, detector, …). After a successful POST, **also** parse the file for `\"detector\"` \u002F `\"detector_type\"` — if it's `\"resnet\"` or `\"transformer\"`, use that value for the `\u002Fcalibrate` call in Step B (detector is a separate API parameter, not consumed by `\u002Fconfig`).\n\nNon-2xx is surfaced — do not silently fall back. Skip this call entirely if the user chose the UI-fallback path.\n\n## UI Fallback Pattern\n\nWhen alignment \u002F layout files aren't on disk, direct the user to the appropriate AMC UI step:\n\n- **Settings missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 3: Parameters**, tune via the settings dialog (or accept defaults), click Save.\" **Also**: before the `\u002Fcalibrate` call, ask the user via `AskUserQuestion` whether to use the `resnet` or `transformer` detector — Step 3 doesn't cover detector choice.\n- **Layout missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 2: Video Configuration**, upload `layout.png` only (do NOT re-upload videos — they're already attached via API\u002FRTSP), click Save.\"\n- **Alignment missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 4: Alignment**, either upload `alignment_data.json` or mark correspondence points on the layout, click Save.\"\n\nWait for user confirmation. For alignment\u002Flayout, verify on disk before continuing:\n\n```bash\n# Project state lives under $VSS_APPS_DIR\u002Fservices\u002Fauto-calibration\u002Fprojects\n# (the path bind-mounted into the MS container in\n#  deploy\u002Fdocker\u002Fservices\u002Fauto-calibration\u002Fms\u002Fcompose.yml).\nHOST_PROJECTS=\"${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\"\n\nls \"$HOST_PROJECTS\u002Fproject_\u003Cproject_id>\u002Fmanual_adjustment\u002F\"\n# Expected: alignment_data.json, layout.png\n```\n\n## Success Criteria\n\n- `project_state == \"COMPLETED\"` after polling.\n- If manual alignment was used: `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Fmanual_adjustment\u002F` contains `alignment_data.json` + `layout.png`.\n- If GT was uploaded: evaluation returns typical thresholds (`Average L2 distance(m)` \u003C 1.5, `Average reprojection error 0(px)` \u003C 5 for your data; \u003C 10 for the bundled sample).\n- No `ERROR` state.\n\n## Key Output Files\n\nUnder `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cproject_id>\u002F`:\n\n```\nproject_\u003Cproject_id>\u002F\n├── manual_adjustment\u002F\n│   ├── alignment_data.json\n│   └── layout.png\n├── output\u002F\n│   ├── single_view_results\u002Fcam_XX\u002F\n│   │   ├── camInfo_hyper_XX.yaml\n│   │   └── trajDump_Stream_0_3d.txt\n│   ├── multi_view_results\u002FBA_output\u002Fresults_ba\u002F\n│   │   ├── initial\u002FcamInfo_XX.yaml\n│   │   └── refined\u002FcamInfo_XX.yaml          # ← final calibration\n│   └── multi_view_results\u002FBA_output\u002Fresults_ba_scaled_world\u002F\n│       └── overlay_img_XX.png               # ← visual overlay for review\n└── calibration.log\n```\n\n## Cross-cutting Troubleshooting\n\nMode-specific issues live in each reference's own troubleshooting table.\n\n| Issue | Fix |\n|---|---|\n| `verify_project` state not `READY` | Confirm videos uploaded\u002Fingested and alignment + layout are present (either via API or via UI manual alignment). Mode-specific upload steps in the reference. |\n| Manual alignment files missing after UI step | User didn't click Save; also verify `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Fmanual_adjustment\u002F` exists. |\n| Calibration stuck `RUNNING` > 90 min | `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` — usually insufficient tracklets (scene too static). See \"Custom Dataset\" guidelines in root `README.md`. |\n| Immediate `ERROR` state | Check video naming: must be `cam_00.mp4`, `cam_01.mp4`, … contiguous (videos mode) \u002F camera_name labels (RTSP mode). |\n| Low L2 but high reprojection | Provide explicit `focal_length` override during input upload (see videos \u002F rtsp references). |\n| VGGT `INIT`, never `READY` | VGGT model not loaded — see [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) Step 2. |\n| Upload timeout | Large videos — bump `timeout=300` to e.g. `600` in the per-mode Python script. |\n| Port scan finds no backend | Backend not running — walk [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) first. |\n\n## For Downstream Skills — MV3DT Export\n\nDownstream consumers (e.g. a Multi-View 3D Tracking skill owned by another team) fetch the MV3DT-format calibration output directly from the microservice. This skill returns the `project_id`; the downstream skill calls:\n\n```\nGET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=amc\n# Response: application\u002Fzip — mv3dt_output.zip containing transforms.yml\n```\n\nFor VGGT-refined output (only available if VGGT ran to `COMPLETED`, see Step E):\n\n```\nGET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=vggt\n# Response: application\u002Fzip — vggt_mv3dt_output.zip\n```\n\nDownstream skill flow:\n1. Call this skill with the user's inputs; capture the printed `project_id`.\n2. Wait for the skill to return (it polls until `COMPLETED` internally).\n3. `GET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=amc` — save the ZIP locally.\n4. If VGGT also ran, optionally fetch `?result_type=vggt` for the refined MV3DT.\n\n## Related Skills\n\n- [`vss-manage-video-io-storage`](..\u002Fvss-manage-video-io-storage\u002FSKILL.md) — VIOS API skill; only the `rtsp` calibration mode depends on VIOS being reachable.\n\nRoot `README.md` \"Custom Dataset\" and \"Calibration Workflow (UI)\" sections document input-video guidelines and the UI-driven alternative to this API flow.\n\nbump:1\n","## Purpose\n\nRun AutoMagicCalib end-to-end on local files, RTSP streams, or the bundled sample dataset and (when needed) deploy the AMC microservice.\n\n## Instructions\n\nFollow the routing tables and step-by-step workflows below. Each section that ends in *workflow*, *quick start*, or *flow* is intended to be executed top-to-bottom. Detailed reference material lives in `references\u002F`; load only the reference needed for the selected input mode.\n\n## Examples\n\nWorked end-to-end examples are kept under `evals\u002F` (each `*.json` manifest contains a runnable scenario) and inline in the per-workflow `curl` blocks below. Run a Tier-3 evaluation with `nv-base validate \u003Cthis-skill-dir> --agent-eval` to replay them.\n\n## Limitations\n\n- Requires the matching VSS profile \u002F microservice to be deployed and reachable from the caller.\n- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.\n- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.\n\n## Troubleshooting\n\n- **Error**: REST call returns connection refused. **Cause**: target microservice not running. **Solution**: probe `\u002Fdocs` or `\u002Fhealth`; redeploy via `vss-deploy-profile` or the matching `vss-deploy-*` skill.\n- **Error**: HTTP 401\u002F403 from NGC pulls. **Cause**: missing\u002Fexpired `NGC_CLI_API_KEY`. **Solution**: `docker login nvcr.io` and re-export the key before retrying.\n- **Error**: container OOM or model fails to load. **Cause**: insufficient GPU memory for the selected profile. **Solution**: switch to a smaller variant or free GPUs via `docker compose down`.\n\n# VSS Generate Video Calibration\n\nRun AutoMagicCalib over one of three input sources and drive the calibration through the microservice REST API. The input-resolution work differs per source; everything from `verify_project` onward is identical and lives in this file. Pick the right input-mode reference and pair it with the [Shared Calibration Tail](#shared-calibration-tail) below.\n\nShared helper references are loaded only when needed:\n- Read [`references\u002Fcommon-steps.md`](references\u002Fcommon-steps.md) when a mode reference needs the shared `create_project`, video-upload, or handoff snippets.\n- Read [`references\u002Fcalibration-tail.md`](references\u002Fcalibration-tail.md) when you need the reusable Python implementation of the verify → calibrate → poll → results tail.\n\n## Input Routing\n\nMatch the user's request to a mode, then load that mode's reference for input collection, mode-specific API calls, and the full Python script.\n\n| User says \u002F has | Mode | Reference |\n|---|---|---|\n| \"launch AMC\" \u002F \"deploy auto-calibration\" \u002F \"set up auto-magic-calib\" \u002F \"start AMC microservice\" | `deploy` | [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) |\n| \"calibrate my videos\" \u002F \"calibrate from video files\" \u002F local `cam_*.mp4` files | `videos` | [`references\u002Fvideos.md`](references\u002Fvideos.md) |\n| \"calibrate RTSP streams\" \u002F \"calibrate from live cameras\" \u002F live RTSP URLs | `rtsp` | [`references\u002Frtsp.md`](references\u002Frtsp.md) |\n| \"test sample dataset\" \u002F \"verify AMC install\" \u002F \"launch and test\" | `sample-dataset` | [`references\u002Fsample-dataset.md`](references\u002Fsample-dataset.md) |\n\n**Disambiguation rule:** if the user is asking to launch \u002F deploy \u002F set up AMC (no calibration verb) → `deploy`. If they provide RTSP URLs → `rtsp`. If they mention local files \u002F a videos directory → `videos`. If they ask to verify install or test the bundled sample → `sample-dataset`. Combined intents (e.g. \"launch AMC and calibrate my videos\") → walk `deploy` first, then the calibration mode. When ambiguous, ask via `AskUserQuestion`.\n\n## Prerequisites (shared across calibration modes)\n\n- AMC microservice + UI running. If not, walk [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) first.\n- Microservice reachable at `http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_PORT:-8010}\u002Fv1\u002Fready` → `{\"code\":0,...}`.\n- Projects directory writable by the container user. If you didn't just deploy (so Step 5 of the deploy reference hasn't run), confirm the write test in [`references\u002Fdeploy-auto-calibration-service.md` § Step 5](references\u002Fdeploy-auto-calibration-service.md#step-5--confirm-the-projects-directory-is-writable) — otherwise the first `create_project` returns `[Errno 13] Permission denied`.\n- Python 3 with `requests` installed (each input-mode reference includes a self-healing venv fallback for direct runs).\n\nMode-specific prerequisites (VIOS for `rtsp`, sample zip for `sample-dataset`) live in the respective references.\n\n## Shared Calibration Tail\n\nThe verify → calibrate → poll → results sequence is identical regardless of input mode. After the mode-specific reference has uploaded videos \u002F ingested RTSP clips \u002F uploaded the bundled sample, run this tail. Use [`references\u002Fcalibration-tail.md`](references\u002Fcalibration-tail.md) for the shared Python snippet.\n\n### Step A — Verify Project\n\n```\nPOST \u002Fv1\u002Fverify_project\u002F\u003Cproject_id>\n```\n\nResponse: `{\"project_state\": \"READY\"}` — must be `READY` before calibrating. If not READY, re-check that videos + alignment + layout are present (either via API or via UI manual alignment).\n\n### Step B — Start Calibration\n\n**Confirm the plan before calibrating.** Whether the settings file and detector were auto-detected or asked, present a short summary and confirm via `AskUserQuestion` before the `POST \u002Fcalibrate`. The resolved values are the defaults, so confirming is one click — but the user can switch the detector or skip an auto-detected settings file. Summarize:\n\n- **Detector** — `resnet` or `transformer` (the value to be sent).\n- **Calibration settings** — the file being applied (path), or default parameters (with the option to tune them in the UI first — see below).\n- **Optional overrides** — ground-truth zip and focal lengths, if any.\n\nThe sample-dataset install-check run uses a fixed `resnet` and can proceed without this confirmation.\n\n```\nPOST \u002Fv1\u002Fcalibrate\u002F\u003Cproject_id>\nContent-Type: application\u002Fjson\n\n{\"detector_type\": \"resnet\"}   # or \"transformer\"\n```\n\n`detector_type` is a separate `\u002Fcalibrate` parameter — **not** consumed by `\u002Fv1\u002Fconfig\u002F\u003Cid>`. If the user provided a calibration settings file, parse it for `\"detector\"` \u002F `\"detector_type\"` and use that value. If the file doesn't specify one, the default (`resnet`) is the value shown in the confirmation above — the user can switch it there before calibrating. If there's no settings file at all, ask the user via `AskUserQuestion`:\n\n- `resnet` — default, fast.\n- `transformer` — slower, better under heavy occlusion.\n\nUI Step 3 (Parameters) does NOT cover detector choice; never assume the user picked one in the UI.\n\n**Also when there's no settings file, ask whether to tune the calibration parameters first** (`AskUserQuestion`):\n\n- **Proceed with the default parameters** — well-suited to typical warehouse scenes; recommended unless the user has specific tuning in mind.\n- **Adjust parameters in the UI first** — open the project, go to Step 3: Parameters, change values, and click Save; then continue.\n\nWait for the user's choice — and, if they choose to tune, for them to confirm they've Saved — before calling `\u002Fcalibrate`.\n\n### Step C — Poll for Completion\n\n```\nGET \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>\n```\n\nPoll every 10 s. `project_info.project_state`:\n\n| State | Meaning |\n|---|---|\n| `RUNNING` | Calibration in progress |\n| `COMPLETED` | Finished |\n| `ERROR` | Failed — pull log via `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` |\n\nWhen calibration starts, surface the project ID, the UI URL (`http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_UI_PORT:-5000}`), and the log endpoint so the user can watch progress while the run proceeds. During `RUNNING`, emit a progress line at least once a minute with elapsed time so a long run doesn't look stalled. On `ERROR`, fetch and show the last lines of `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` before stopping. Live logs can also be streamed via `GET \u002Fv1\u002Fcalibrate\u002F\u003Cproject_id>\u002Flog\u002F\u003Ctype>\u002Fstream`.\n\nTypical time: **10–60 min** (your-own videos), **10–30 min** (bundled sample).\n\n### Step D — Results\n\n```\nGET \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>                    # project state\nGET \u002Fv1\u002Fresult\u002F\u003Cproject_id>\u002Fevaluation_statistics        # only if GT uploaded\nGET \u002Fv1\u002Fresult\u002F\u003Cproject_id>\u002Foverlay_image                # visual overlay (PNG)\nGET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cproject_id>\u002Flog                   # calibration log\n```\n\nEvaluation response includes `Average L2 distance(m)` and `Average reprojection error 0(px)`. Evaluation metrics are produced **only when a ground-truth `GT.zip` was uploaded** — a missing `evaluation_statistics` result is normal otherwise and is not the end of result reporting.\n\nAfter `COMPLETED`, always give the user a way to review the result for that exact project, regardless of whether metrics exist:\n\n- **UI** — `http:\u002F\u002F\u003CHOST_IP>:${VSS_AUTO_CALIBRATION_UI_PORT:-5000}`; open the project, then the Results page to view the overlay.\n- **Overlay image on disk** — `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Foutput\u002Fmulti_view_results\u002FBA_output\u002Fresults_ba_scaled_world\u002Foverlay_img_*.png` (single-camera projects use `output\u002Fsingle_view_results\u002Fcam_00\u002Fverification_map_overlay.png`).\n- **Project files** — `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002F`.\n\n### Step E — VGGT Refinement\n\nAfter the AMC run completes, always check `vggt_state` in project info. VGGT model staging is optional during setup and must not block the AMC result, but post-AMC handling follows the state:\n\n- If `vggt_state == \"READY\"` and the user explicitly requested VGGT refinement or staged VGGT during this setup flow, run VGGT refinement without asking again.\n- If `vggt_state == \"READY\"` but VGGT was already staged before this request and the user has not asked for VGGT-refined output, ask via `AskUserQuestion` whether to run refinement before starting it.\n- If VGGT is not ready, skip refinement and mention that VGGT refinement is available after staging the model (see [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) Step 2).\n\n```\nPOST \u002Fv1\u002Fvggt\u002Fcalibrate\u002F\u003Cproject_id>\nGET  \u002Fv1\u002Fget_project_info\u002F\u003Cproject_id>                    # poll vggt_state\nGET  \u002Fv1\u002Fvggt_results\u002F\u003Cproject_id>\u002Fevaluation_statistics  # VGGT metrics\n```\n\n## Settings File + Detector Pattern\n\nOptional across all three modes. When the user provides a JSON settings file (typically exported from UI Step 3 Download), POST it verbatim:\n\n```\nPOST \u002Fv1\u002Fconfig\u002F\u003Cproject_id>\nContent-Type: application\u002Fjson\n\n\u003Cfile contents, posted as-is>\n```\n\nThe file replaces what the user would otherwise tune in UI Step 3 (rectification, bundle-adjustment, evaluation knobs, detector, …). After a successful POST, **also** parse the file for `\"detector\"` \u002F `\"detector_type\"` — if it's `\"resnet\"` or `\"transformer\"`, use that value for the `\u002Fcalibrate` call in Step B (detector is a separate API parameter, not consumed by `\u002Fconfig`).\n\nNon-2xx is surfaced — do not silently fall back. Skip this call entirely if the user chose the UI-fallback path.\n\n## UI Fallback Pattern\n\nWhen alignment \u002F layout files aren't on disk, direct the user to the appropriate AMC UI step:\n\n- **Settings missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 3: Parameters**, tune via the settings dialog (or accept defaults), click Save.\" **Also**: before the `\u002Fcalibrate` call, ask the user via `AskUserQuestion` whether to use the `resnet` or `transformer` detector — Step 3 doesn't cover detector choice.\n- **Layout missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 2: Video Configuration**, upload `layout.png` only (do NOT re-upload videos — they're already attached via API\u002FRTSP), click Save.\"\n- **Alignment missing** → \"Open UI project `\u003Cproject_id>`, go to **Step 4: Alignment**, either upload `alignment_data.json` or mark correspondence points on the layout, click Save.\"\n\nWait for user confirmation. For alignment\u002Flayout, verify on disk before continuing:\n\n```bash\n# Project state lives under $VSS_APPS_DIR\u002Fservices\u002Fauto-calibration\u002Fprojects\n# (the path bind-mounted into the MS container in\n#  deploy\u002Fdocker\u002Fservices\u002Fauto-calibration\u002Fms\u002Fcompose.yml).\nHOST_PROJECTS=\"${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\"\n\nls \"$HOST_PROJECTS\u002Fproject_\u003Cproject_id>\u002Fmanual_adjustment\u002F\"\n# Expected: alignment_data.json, layout.png\n```\n\n## Success Criteria\n\n- `project_state == \"COMPLETED\"` after polling.\n- If manual alignment was used: `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Fmanual_adjustment\u002F` contains `alignment_data.json` + `layout.png`.\n- If GT was uploaded: evaluation returns typical thresholds (`Average L2 distance(m)` \u003C 1.5, `Average reprojection error 0(px)` \u003C 5 for your data; \u003C 10 for the bundled sample).\n- No `ERROR` state.\n\n## Key Output Files\n\nUnder `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cproject_id>\u002F`:\n\n```\nproject_\u003Cproject_id>\u002F\n├── manual_adjustment\u002F\n│   ├── alignment_data.json\n│   └── layout.png\n├── output\u002F\n│   ├── single_view_results\u002Fcam_XX\u002F\n│   │   ├── camInfo_hyper_XX.yaml\n│   │   └── trajDump_Stream_0_3d.txt\n│   ├── multi_view_results\u002FBA_output\u002Fresults_ba\u002F\n│   │   ├── initial\u002FcamInfo_XX.yaml\n│   │   └── refined\u002FcamInfo_XX.yaml          # ← final calibration\n│   └── multi_view_results\u002FBA_output\u002Fresults_ba_scaled_world\u002F\n│       └── overlay_img_XX.png               # ← visual overlay for review\n└── calibration.log\n```\n\n## Cross-cutting Troubleshooting\n\nMode-specific issues live in each reference's own troubleshooting table.\n\n| Issue | Fix |\n|---|---|\n| `verify_project` state not `READY` | Confirm videos uploaded\u002Fingested and alignment + layout are present (either via API or via UI manual alignment). Mode-specific upload steps in the reference. |\n| Manual alignment files missing after UI step | User didn't click Save; also verify `${VSS_APPS_DIR}\u002Fservices\u002Fauto-calibration\u002Fprojects\u002Fproject_\u003Cid>\u002Fmanual_adjustment\u002F` exists. |\n| Calibration stuck `RUNNING` > 90 min | `GET \u002Fv1\u002Famc\u002Fcalibrate\u002F\u003Cid>\u002Flog` — usually insufficient tracklets (scene too static). See \"Custom Dataset\" guidelines in root `README.md`. |\n| Immediate `ERROR` state | Check video naming: must be `cam_00.mp4`, `cam_01.mp4`, … contiguous (videos mode) \u002F camera_name labels (RTSP mode). |\n| Low L2 but high reprojection | Provide explicit `focal_length` override during input upload (see videos \u002F rtsp references). |\n| VGGT `INIT`, never `READY` | VGGT model not loaded — see [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) Step 2. |\n| Upload timeout | Large videos — bump `timeout=300` to e.g. `600` in the per-mode Python script. |\n| Port scan finds no backend | Backend not running — walk [`references\u002Fdeploy-auto-calibration-service.md`](references\u002Fdeploy-auto-calibration-service.md) first. |\n\n## For Downstream Skills — MV3DT Export\n\nDownstream consumers (e.g. a Multi-View 3D Tracking skill owned by another team) fetch the MV3DT-format calibration output directly from the microservice. This skill returns the `project_id`; the downstream skill calls:\n\n```\nGET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=amc\n# Response: application\u002Fzip — mv3dt_output.zip containing transforms.yml\n```\n\nFor VGGT-refined output (only available if VGGT ran to `COMPLETED`, see Step E):\n\n```\nGET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=vggt\n# Response: application\u002Fzip — vggt_mv3dt_output.zip\n```\n\nDownstream skill flow:\n1. Call this skill with the user's inputs; capture the printed `project_id`.\n2. Wait for the skill to return (it polls until `COMPLETED` internally).\n3. `GET \u002Fv1\u002Fresult\u002F{project_id}\u002Fmv3dt_result?result_type=amc` — save the ZIP locally.\n4. If VGGT also ran, optionally fetch `?result_type=vggt` for the refined MV3DT.\n\n## Related Skills\n\n- [`vss-manage-video-io-storage`](..\u002Fvss-manage-video-io-storage\u002FSKILL.md) — VIOS API skill; only the `rtsp` calibration mode depends on VIOS being reachable.\n\nRoot `README.md` \"Custom Dataset\" and \"Calibration Workflow (UI)\" sections document input-video guidelines and the UI-driven alternative to this API flow.\n\nbump:1\n",{"name":5,"license":15,"metadata":49,"description":7},{"tags":50,"version":51,"github-url":52},"nvidia blueprint operational","3.2.0","https:\u002F\u002Fgithub.com\u002FNVIDIA-AI-Blueprints\u002Fvideo-search-and-summarization","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".agent\u002Fskills\u002Fvss-generate-video-calibration","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 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xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[130,133],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[137,138,139,140,141,33,142,143,144,145,146],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":155,"publishedAt":44},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[153,154],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},[156,157,139,158,159,160,161,162,33,163,164,165,38,166,142,143,167,168,42,169],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","apple","apps","native","objective",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":180,"publishedAt":44},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[176,179],{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},[181,182,183,184,185,186,187,188,33,189,190,191,192,193,194,42,195],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":204,"publishedAt":44},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[202,203],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},[205,139,206,158,207,208,209,210,141,33,211,38,142,143,212,213],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":229,"publishedAt":44},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[220,223,224],{"categoryNo":221,"categoryName":222,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":225,"categoryName":226,"taxonomyType":227,"taxonomyTypeDesc":228},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,143,248],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":250,"name":251,"nameZh":6,"description":252,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":127,"licenseFileUrl":6,"sourceHtmlUrl":253,"categories":254,"keywords":257,"publishedAt":44},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[255,256],{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":225,"categoryName":226,"taxonomyType":227,"taxonomyTypeDesc":228},[230,232,258,239,242,33,259,260,166,247,261,143,167,262,42,263,264],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]