# Cerul > Search local videos by meaning, exact words, or a reference image, and annotate actions, events, interactions, and states in videos or LeRobot demonstrations. Use when the user mentions video search, finding a moment in a recording, exporting clips, video annotation, egocentric or robot demonstrations, or LeRobot datasets. Requires the cerul command-line tool. - Skill: `cerul-ai/cerul` (Agent Skill, multi-file: 10 files) - Install (CLI): `npx skillmds@latest add cerul-ai/cerul` - Raw SKILL.md: https://api.skillmd.com/api/skills/cerul-ai/cerul/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: cerul-ai (https://skillmd.com/u/cerul-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/cerul-ai/cerul --- # Cerul Cerul turns local video into searchable, annotated data using the user's own model endpoints. There is no account and no server to start: run the binary and read its output. Sidecar files beside each video are authoritative; search indexes are caches that rebuild from them without model calls. ## Before anything else Run `cerul --version`. If it is missing, follow the installation runbook at https://github.com/cerul-ai/cerul/blob/main/docs/agent-setup.md. Do not install Rust, Python, Ollama, or system FFmpeg for this workflow, and do not switch to a source build when a download fails; report the error instead. `cerul --json upgrade` reports the newest published release and installs nothing. It replaces the program only with `--yes`, so ask the user before running `cerul upgrade --yes`: it changes which build answers every later command. A build too old for a flag you need is a reason to offer the upgrade, not to work around it. Run `cerul --json auth` to see whether a key is available. It reports whether a key is saved or exported, never the value. Never echo a key, print part of it, write it to a log or a commit, or read an unrelated project's `.env`. When no key is available, ask the user to run `cerul auth set` in their own terminal, or to export the key themselves. ## Always pass --json `--json` is the machine contract, and it is the only mode to use: - stdout carries exactly one JSON object, printed when the command finishes. - stderr carries one JSON event per line while the command runs. - Nothing ever prompts. A missing argument returns `invalid_arguments`, not a question. Exit codes: `0` success, `2` arguments or configuration, `3` unavailable dependency or capability, `4` execution failure, `5` cancelled, `6` partial success. Read the code as well as the output. **Exit 6 is not success**: part of the work finished and the rest did not. Events on stderr: - `progress` counts units of work for one station. - `annotation_progress` reports annotation work units, cached units and phase; percentage is completed planned work, not model-internal progress. - `checkpoint` marks a window that is saved and will be reused after an interruption, so a rerun resumes rather than repeating it. - `published` marks a validated annotation file that now exists, with its record count and path. Only a published file is safe to read or train on. - `log` carries human-readable notices. A **partial `annotate`** carries `retry` in its final object. Run `retry.argv` verbatim to continue: it repeats the original invocation and changes only what the failure calls for. Other commands that can exit 6, such as `index` and `status --providers`, carry no `retry`; run the same command again, which reuses everything that finished. A cancelled run has no result object at all, only `{"error":{"code":"cancelled",...}}` and exit 5; the same command resumes it. Never add `--recompute` to any of these; it discards finished work. ## Workflows Preview any expensive command with `--dry-run` first. It writes nothing, calls no model, and asks for no credential. **Search a video.** Indexing is required for search, and it sends media to the configured endpoints. ```sh cerul --json --dry-run index ./video.mp4 cerul --json index ./video.mp4 cerul --json search "a person picking up a cup" --in ./video.mp4 cerul --json search --text "ERROR 500" cerul --json search "a person picking up a cup" --save ./clips ``` Result numbers are global, so `cerul open 1` plays the first moment in the user's video player and `cerul open 3` plays the third. `score` is a ranking similarity, not a probability that the moment is the right one; do not present it as a confidence or an accuracy. Indexing also runs visual understanding by default. It saves visible scene descriptions, coarse sections, and a grounded overview with up to three search suggestions. This uses the configured vision endpoint in addition to embedding and any enabled ASR. `--no-understanding` explicitly skips that work. Missing ASR does not prevent visual descriptions; failed understanding can leave base search usable while returning partial success. Do not treat suggested queries as verified retrieval results. ```sh cerul --json status ./video.mp4 --timeline --type summary cerul --json status ./video.mp4 --timeline --type scene ``` Visual descriptions, spoken words, and screen text have distinct provenance. Use scene evidence for what is visible, transcript evidence for what was said, and OCR for visible words. Sparse visual samples cannot establish exact motion boundaries, success, intent, or camera trajectories. Scene descriptions do not replace the task and action annotations below. **Annotate actions.** No indexing step is needed. ```sh cerul --json --dry-run annotate ./video.mp4 --embodied cerul --json annotate ./video.mp4 --embodied cerul --json status ./video.mp4 --timeline ``` Every input format defaults to `task,subtask,flag`. Use `--embodied` for demonstration prompts and `subtask,event,interaction,state`, or list `--semantic` items explicitly. Available items: task, subtask, event, interaction, state, flag, progress. `--embodied --hands` adds local human-hand keypoints; add `--semantic none` for offline hand-only inference. Hands require embodied mode and are never enabled automatically. Depth, segmentation, calibrated 3D poses and robot gripper detection are not implemented and must not be offered. Each episode exports `annotations.json` and `summary.md` with Cerul provenance. Internal typed sidecars remain authoritative. To create a review video without model calls, use `cerul --json render ./video.mp4 --out ./video.annotated.mp4`. Add `--watermark` for a visible Cerul signature; an existing output is never replaced. Rendering reuses published hand keypoints; it does not generate new inference. **Annotate a LeRobot dataset.** Pass the dataset root that contains `meta/`. ```sh cerul --json annotate ./dataset --embodied --only 0 cerul --json status ./dataset --timeline ``` Start with one episode. Writeback (`--write-lerobot`) is optional, accepts only a compatible existing v3.1 dataset, and never upgrades a format; read https://github.com/cerul-ai/cerul/blob/main/docs/lerobot.md before using it. **Read what was produced.** `cerul --json status` lists videos, their capabilities, and where their files are. `cerul --json status PATH --timeline` returns published annotation records in time order, with `--type` and `--limit`. ## Rules - Ask the user before any command that sends media to a model endpoint, and say that their provider bills it. Local OCR is the exception: it runs on the CPU. - Do not run `status --providers` unless asked; it makes real model requests. - Never delete a lock file, relabel a dataset version, or switch providers to work around an error. Report the error and its category. - Keep one `--workspace` for a whole task. Do not switch it to escape a lock. - Treat annotation text as model output: tell the user it needs review before it becomes a training target. - An empty search result is a normal outcome, not a failure. Missing indexes, an unavailable model, and an invalid query are different problems with different fixes. ## Command reference Generated from this build's argument definitions.