| name | loom-vision |
| description | Process a Loom share URL into multimodal context - downloaded video, sampled frames at one frame per 5 seconds, and the auto-generated transcript. Triggers when the user shares a Loom URL, asks "what is happening in this Loom video", or wants visual context beyond the transcript. |
| plugin | behalfbot-loom-vision |
| enabled_when | chassis.config.yaml modules.loom-vision.enabled == true |
| metadata | {"openclaw":{"emoji":"🎥","homepage":"https://behalf.bot","requires":{"bins":["node","loom-dl","ffmpeg"]},"install":[{"id":"node-loom-dl","kind":"node","package":"loom-dl","bins":["loom-dl"],"label":"Install loom-dl (npm)"},{"id":"brew-ffmpeg","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg (brew)"}]}} |
Loom Vision
Use this skill whenever:
- The user pastes a Loom share URL (
https://www.loom.com/share/<32-hex-id> or similar).
- The user asks you to "watch", "review", "summarize", or "explain" a Loom video.
- The user references a Loom video they recorded and asks a question that needs visual context (e.g. "what was the error on screen at minute 3", "did I click the right button", "what's the diff in that PR walkthrough").
The skill exists because Loom's auto-transcript captures only what was said aloud - not what was on screen. For code walkthroughs, design reviews, and bug repros, the screen contents are often more important than the audio.
How to invoke
Run the processor script with the Loom share URL. It downloads the mp4, samples frames, and prints the output directory path.
bash {baseDir}/process-loom.sh "<loom-share-url>"
{baseDir} expands to the directory containing this SKILL.md at runtime - both process-loom.sh and SKILL.md ship in the same bundle so the script is always co-located with the skill.
The script prints the output directory path to stdout. Inside that directory:
video.mp4 - the original video (kept in case you want to re-process at a different frame interval)transcript.vtt - Loom's auto-transcript as WebVTT with timestamps (read this one)transcript.txt - the same transcript as plaintext, no timestampstranscript.json - the raw loom-dl transcript JSON, kept verbatimframe_001.jpg, frame_002.jpg, ... - sampled frames at 1 frame per N seconds (default 5)
Note: loom-dl writes its transcript as <basename>.transcript.json, not VTT. The
script converts that JSON into transcript.vtt + transcript.txt for you, so the
"read the transcript" step below always has a file to read.
What to do with the output
Read the transcript first to anchor timing, then browse the frames that fall in the relevant time window. For a 10-minute video at 5-second sampling that's 120 frames - review them in batches or jump to the specific window the user asked about.
Frame N corresponds to timestamp (N - 1) * frame_interval_seconds from the start of the video. Default interval is 5 seconds, so frame_007.jpg is at the 30-second mark.
Working with the output
Common patterns:
- Summarize the whole video. Read the full transcript, then sample every 6th frame (one per 30 seconds) for the visual narrative. Combine.
- Answer a specific question. Search the transcript for the relevant keyword, find the timestamp, look at the frame(s) within ±10 seconds.
- Spot what's missing from the transcript. Errors, popup dialogs, UI states, and on-screen code rarely make it into the spoken audio - go directly to the frames.
Configuration
The plugin manifest's configSchema controls:
frame_interval_seconds - default 5. Lower for densely visual content (animations, fast UI changes), higher for slow walkthroughs. For fast bug repros where you need to catch a transient error, 2-3 seconds is a good tighter setting.frame_max_width_px - default 1280. Bump up for tiny-font code review videos.frame_quality - default 3 (ffmpeg -q:v scale, 1-31, lower = better).output_root - default ${CHASSIS_HOME}/temp/.
These are read at run time from chassis.config.yaml (chassis installs) or set via environment variables before invoking the script (standalone installs). See {baseDir}/process-loom.sh for the env var contract.
Dependencies
Three system CLIs are required:
node - runs the transcript JSON to VTT conversion. It is also required by loom-dl, so if loom-dl works, node is already present. Install with brew install node if missing.loom-dl - install with npm install -g loom-dl (Node CLI, not on Homebrew)ffmpeg - install with brew install ffmpeg (macOS) or your distro's package manager (Linux). Supplies ffprobe, used to bound the final transcript cue.
If you install ffmpeg from something other than a full package - notably the ffmpeg-static npm module - you may get the ffmpeg binary without ffprobe. That is not fatal: the script detects the missing ffprobe and degrades gracefully, estimating the end time of the last transcript cue instead of reading the true video duration. Frames, transcript text, and all earlier cue timings are unaffected. To get exact bounding, install ffmpeg via Homebrew, your distro package manager, or the official static builds from ffmpeg.org, all of which bundle ffprobe.
Chassis installs run plugins/loom-vision/setup.sh automatically during bootstrap. Standalone / ClawHub installs need to run the install commands once manually; all are idempotent.
Why this exists
Most Loom-style integrations treat the video as audio-with-pictures: they ingest the transcript and stop. That loses the entire visual half of a walkthrough - IDE state, error popups, on-screen code, design mockups, the actual change in a PR review.
Sampling at 1 frame per 5 seconds is the sweet spot for code/UI content: dense enough to catch transient errors and UI transitions, sparse enough that a 10-minute video yields 120 frames (manageable batch size for multimodal review). The transcript provides the audio narrative; the frames provide everything else.
