---

name: run-slam description: "Guides MASt3R-SLAM runtime commands for videos, RGB folders, live cameras, benchmark sequences, configs, calibration, visualization, and outputs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

run-slam

Use this sub-skill when the user wants to run MASt3R-SLAM on a concrete input or needs help choosing runtime flags, config files, calibration, headless mode, or output paths.

Triggers

• "run MASt3R-SLAM on a video"
• "process an RGB image folder"
• "use RealSense or webcam"
• "which config/calibration file should I use"
• "why did load_dataset choose the wrong format"
• "where are trajectory or PLY outputs saved"
• "run headless/no visualization"

Prerequisites

Before any real run, make sure setup-and-backends has verified:

• CUDA torch and mast3r_slam_backends import.
• Three MASt3R checkpoint assets in the runtime checkpoint directory.
• Editable MASt3R-SLAM install, or a user-provided checkout containing the runtime launcher.

First reads and scripts

• references/cli-reference.md for verified flags.
• references/configuration.md for YAML templates and config inheritance.
• references/data-formats.md for dataset/video/live input layouts.
• references/workflows.md for concrete run recipes.
• references/troubleshooting.md for runtime failures.
• scripts/write_config_templates.py to write bundled base, calib, eval_*, eth3d, and intrinsics templates.
• scripts/validate_inputs.py to classify and check a dataset path.
• scripts/run_mast3r_slam.py to build a safe dry-run command and optionally execute it.

Recommended runtime flow

1. Generate config templates if you are not using an existing checked-out config:

   python sub-skills/run-slam/scripts/write_config_templates.py --output-dir <config-dir>
   

2. Validate the input path:

   python sub-skills/run-slam/scripts/validate_inputs.py --dataset <dataset-or-video>
   

3. Build the command without executing it:

   python sub-skills/run-slam/scripts/run_mast3r_slam.py \
     --repo-root <MASt3R-SLAM-checkout> \
     --dataset <dataset-or-video> \
     --config <config-dir>/base.yaml \
     --no-viz \
     --dry-run
   

4. Only after confirming GPU, checkpoints, input data, and expected runtime, rerun with --execute.

Boundary decisions

• This sub-skill owns single-run command construction and input validation.
• It routes benchmark-suite loops and metrics to evaluation.
• It routes install/CUDA/checkpoint failures to setup-and-backends.

Common choices

• Use --no-viz in headless containers, CI, SSH sessions, or benchmark loops.
• Use a no-calibration config for unknown intrinsics; use a calibration config plus --calib only when you have valid width/height/intrinsics.
• Use --save-as <name> when you need deterministic output paths under logs/.
• For videos, install torchcodec only if MP4 decoding speed is the blocker; OpenCV fallback remains available.