cerul-ai/cerul/prompts

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.

Commands can return exit 6 for partial results. Repeat the same command to reuse completed work; do not add --recompute unless fresh processing is intended.

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.

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 builds video embeddings, OCR, and available speech/text search data. It does not call the vision model or generate scene descriptions, sections, or summaries. Use cerul analyze ./video.mp4 for scenes and an overview. Existing analysis and cached description vectors remain readable and searchable; indexing does not refresh them. Search suggestions reuse current evidence without model generation. Do not treat suggested queries as verified retrieval results.

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.

Inspect performance. cerul --json diagnostics reads the latest index/analyze stage timings, request latency and cache counts. Stage wall times overlap; use invocation elapsed time for total throughput. This makes no model calls.

Analyze a video. No indexing step is needed.

cerul --json analyze ./video.mp4
cerul --json --dry-run analyze ./videos/

Without options, returns scenes, chapters and an overview. Add --prompt "Question" for a focused answer, repeat --image ./reference.png for comparison images, and use --from 00:30 --to 01:10 for a half-open episode time range. No embeddings are generated. Focused answers use at most 120 sampled frames and valid cached in-range text; sparse samples cannot establish continuous motion or absence. Results include the actual sample timestamps, reference hashes and limitations. Reference images are not evidence of occurrence in the video.

--stream --json emits provisional analysis_delta NDJSON on stderr; stdout contains only the final structured report. Never treat a delta as validated evidence. Exit 6 and per-stream errors mean incomplete analysis. Cached answers emit one delta marked cached: true. Question-specific results preserve full video scene/overview records; --recompute refreshes the selected request. The fixed response schema is published; arbitrary user JSON schemas are not accepted. Preserve errors and coverage when interpreting results.

Clip a video. clip cuts short clips from one video by intent, working from the transcript alone: no index, no embeddings, no frames sent anywhere.

cerul --json --dry-run clip ./video.mp4
cerul --json clip ./video.mp4
cerul --json clip ./video.mp4 "more about pricing" --max 45s
cerul --json clip ./video.mp4 --export 1,3,5

Dry-run first and tell the user that the audio will be sent to their speech endpoint (sixty-second windows) and the transcript text to their text endpoint; ask before the real run. Do not ask which angle the user wants: run the bare command, which proposes across every angle the brief suggests and the presets for the video's content type. Present the numbered proposals in plain language, grouped by chapter and angle: the quote, the reason, and the duration from start_us and end_us. The quote is the only text verified against the transcript; never present reason or title as fact beyond it. Then ask what to keep or change. Turn corrections in the user's own words into a new run: a wish like "more about pricing" or "nothing from the intro" becomes a quoted positional query or --angle text passed through verbatim (presets numbers, claims, stories, howto, questions, disagreements, definitions also work), and "shorter" or "longer" adjusts --min/--max. Run --export with the chosen numbers (it reuses the cached run and makes no model call; --aspect 9:16 for vertical video, --credit to burn a source line; captions are on by default). Report the exports[] file paths: each clip has an .mp4, an .srt and a .json provenance sidecar. Exit 6 means some clips failed to render; their error says why.

Robotics workflows. Embodied labels, human-hand tracking, LeRobot processing and review-video rendering belong to the separate cerul-robotics CLI and skill: https://github.com/cerul-ai/cerul-robotics. Existing records remain readable here.

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 analysis text as model output and keep its source timestamps available.
• 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.